{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}/Users/rpm47/Dropbox/0001 Academic Projects/Ongoing/0155 Voting Abroad Purpose/Replication/Upload//Experiment1Log.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}12 Jan 2025, 09:28:57
{txt}
{com}. 
. use "${c -(}MyProject{c )-}ProlificData.dta"
{txt}
{com}. 
. 
. ********************************************************************************
. * Power Calculations
. ********************************************************************************
.         
. /// Produces Figure A1, "Humanitarian Experiment, power calcualtion"
> 
. local technical i.order i.mobile i.attention_color
{txt}
{com}. local demographics age i.female i.white i.hispanic i.college i.hhi_num i.pid7 i.employed
{txt}
{com}. 
. 
. reg human_main_DV_dichot1 i.humanitarian_treat_num3  `demographics' `technical' ///
>                 if humanitarian_treat_num3 >=1 

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       934
{txt}{hline 13}{c +}{hline 34}   F(23, 910)      = {res}     3.66
{txt}       Model {c |} {res}  19.753805        23  .858861087   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 213.741912       910  .234881222   {txt}R-squared       ={res}    0.0846
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0615
{txt}       Total {c |} {res} 233.495717       933  .250263363   {txt}Root MSE        =   {res} .48465

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}human_main_DV~1{col 17}{c |} Coefficient{col 29}  Std. err.{col 41}      t{col 49}   P>|t|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
humanitarian_~3 {c |}
{space 8}Muslim  {c |}{col 17}{res}{space 2}-.0452339{col 29}{space 2}  .032047{col 40}{space 1}   -1.41{col 49}{space 3}0.158{col 57}{space 4}-.1081286{col 70}{space 3} .0176608
{txt}{space 12}age {c |}{col 17}{res}{space 2}-.0033464{col 29}{space 2} .0012402{col 40}{space 1}   -2.70{col 49}{space 3}0.007{col 57}{space 4}-.0057804{col 70}{space 3}-.0009124
{txt}{space 15} {c |}
{space 9}female {c |}
{space 8}Female  {c |}{col 17}{res}{space 2} .0715031{col 29}{space 2} .0332624{col 40}{space 1}    2.15{col 49}{space 3}0.032{col 57}{space 4} .0062232{col 70}{space 3}  .136783
{txt}{space 15} {c |}
{space 10}white {c |}
{space 1}White ID Only  {c |}{col 17}{res}{space 2}-.0110966{col 29}{space 2} .0412586{col 40}{space 1}   -0.27{col 49}{space 3}0.788{col 57}{space 4}-.0920697{col 70}{space 3} .0698764
{txt}{space 15} {c |}
{space 7}hispanic {c |}
{space 6}Hispanic  {c |}{col 17}{res}{space 2} .0108184{col 29}{space 2} .0549266{col 40}{space 1}    0.20{col 49}{space 3}0.844{col 57}{space 4}-.0969792{col 70}{space 3}  .118616
{txt}{space 15} {c |}
{space 8}college {c |}
College Degree  {c |}{col 17}{res}{space 2}-.0629862{col 29}{space 2} .0360713{col 40}{space 1}   -1.75{col 49}{space 3}0.081{col 57}{space 4}-.1337789{col 70}{space 3} .0078065
{txt}{space 15} {c |}
{space 8}hhi_num {c |}
$25,000-$5~000  {c |}{col 17}{res}{space 2} .0143103{col 29}{space 2} .0479144{col 40}{space 1}    0.30{col 49}{space 3}0.765{col 57}{space 4}-.0797253{col 70}{space 3}  .108346
{txt}$50,000-$1~000  {c |}{col 17}{res}{space 2}-.0193533{col 29}{space 2}  .048905{col 40}{space 1}   -0.40{col 49}{space 3}0.692{col 57}{space 4} -.115333{col 70}{space 3} .0766263
{txt}$100,000-$~000  {c |}{col 17}{res}{space 2}-.0491241{col 29}{space 2}  .058922{col 40}{space 1}   -0.83{col 49}{space 3}0.405{col 57}{space 4}-.1647628{col 70}{space 3} .0665147
{txt}More than $2..  {c |}{col 17}{res}{space 2} .1494694{col 29}{space 2} .1246447{col 40}{space 1}    1.20{col 49}{space 3}0.231{col 57}{space 4}-.0951551{col 70}{space 3}  .394094
{txt}{space 15} {c |}
{space 11}pid7 {c |}
{space 6}Weak Dem  {c |}{col 17}{res}{space 2}-.0166888{col 29}{space 2} .0553068{col 40}{space 1}   -0.30{col 49}{space 3}0.763{col 57}{space 4}-.1252325{col 70}{space 3} .0918548
{txt}{space 6}Lean Dem  {c |}{col 17}{res}{space 2}-.1620052{col 29}{space 2} .0554926{col 40}{space 1}   -2.92{col 49}{space 3}0.004{col 57}{space 4}-.2709136{col 70}{space 3}-.0530968
{txt}{space 3}Independent  {c |}{col 17}{res}{space 2}-.2153457{col 29}{space 2} .0543073{col 40}{space 1}   -3.97{col 49}{space 3}0.000{col 57}{space 4}-.3219277{col 70}{space 3}-.1087636
{txt}{space 6}Lean GOP  {c |}{col 17}{res}{space 2}-.1471426{col 29}{space 2} .0686842{col 40}{space 1}   -2.14{col 49}{space 3}0.032{col 57}{space 4}-.2819406{col 70}{space 3}-.0123447
{txt}{space 6}Weak GOP  {c |}{col 17}{res}{space 2}-.1588462{col 29}{space 2} .0519278{col 40}{space 1}   -3.06{col 49}{space 3}0.002{col 57}{space 4}-.2607584{col 70}{space 3}-.0569341
{txt}{space 4}Strong GOP  {c |}{col 17}{res}{space 2}-.1631743{col 29}{space 2} .0592932{col 40}{space 1}   -2.75{col 49}{space 3}0.006{col 57}{space 4}-.2795417{col 70}{space 3}-.0468069
{txt}{space 15} {c |}
{space 7}employed {c |}
Not in Workf..  {c |}{col 17}{res}{space 2} .0623955{col 29}{space 2} .0612468{col 40}{space 1}    1.02{col 49}{space 3}0.309{col 57}{space 4}-.0578058{col 70}{space 3} .1825969
{txt}{space 5}Full time  {c |}{col 17}{res}{space 2} .0142624{col 29}{space 2} .0466105{col 40}{space 1}    0.31{col 49}{space 3}0.760{col 57}{space 4}-.0772142{col 70}{space 3}  .105739
{txt}{space 5}Part time  {c |}{col 17}{res}{space 2}-.0055071{col 29}{space 2} .0516986{col 40}{space 1}   -0.11{col 49}{space 3}0.915{col 57}{space 4}-.1069695{col 70}{space 3} .0959552
{txt}{space 15} {c |}
{space 10}order {c |}
{space 13}2  {c |}{col 17}{res}{space 2}-.0030388{col 29}{space 2} .0397567{col 40}{space 1}   -0.08{col 49}{space 3}0.939{col 57}{space 4}-.0810642{col 70}{space 3} .0749866
{txt}{space 13}3  {c |}{col 17}{res}{space 2}-.1795028{col 29}{space 2} .0398869{col 40}{space 1}   -4.50{col 49}{space 3}0.000{col 57}{space 4}-.2577837{col 70}{space 3}-.1012218
{txt}{space 15} {c |}
{space 7}1.mobile {c |}{col 17}{res}{space 2} .0088636{col 29}{space 2} .0352534{col 40}{space 1}    0.25{col 49}{space 3}0.802{col 57}{space 4}-.0603238{col 70}{space 3} .0780511
{txt}1.attention_c~r {c |}{col 17}{res}{space 2}-.2629055{col 29}{space 2} .1746936{col 40}{space 1}   -1.50{col 49}{space 3}0.133{col 57}{space 4}-.6057546{col 70}{space 3} .0799436
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} 1.089318{col 29}{space 2}  .190115{col 40}{space 1}    5.73{col 49}{space 3}0.000{col 57}{space 4} .7162028{col 70}{space 3} 1.462432
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. eststo power_mod_1
{txt}
{com}. 
. logit human_main_DV_dichot1 i.humanitarian_treat_num3  `demographics' `technical' if humanitarian_treat_num3 >=1

{res}{txt}Iteration 0:{space 2}Log likelihood = {res: -647.3909}  
Iteration 1:{space 2}Log likelihood = {res:-606.30042}  
Iteration 2:{space 2}Log likelihood = {res:-606.23525}  
Iteration 3:{space 2}Log likelihood = {res:-606.23524}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:934}
{txt}{col 57}{lalign 13:LR chi2({res:23})}{col 70} = {res}{ralign 6:82.31}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 10:-606.23524}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0636}

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}human_main_DV~1{col 17}{c |} Coefficient{col 29}  Std. err.{col 41}      z{col 49}   P>|z|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
humanitarian_~3 {c |}
{space 8}Muslim  {c |}{col 17}{res}{space 2}-.1943842{col 29}{space 2}  .138167{col 40}{space 1}   -1.41{col 49}{space 3}0.159{col 57}{space 4}-.4651865{col 70}{space 3} .0764181
{txt}{space 12}age {c |}{col 17}{res}{space 2}-.0146613{col 29}{space 2} .0053814{col 40}{space 1}   -2.72{col 49}{space 3}0.006{col 57}{space 4}-.0252087{col 70}{space 3} -.004114
{txt}{space 15} {c |}
{space 9}female {c |}
{space 8}Female  {c |}{col 17}{res}{space 2} .3108048{col 29}{space 2} .1438815{col 40}{space 1}    2.16{col 49}{space 3}0.031{col 57}{space 4} .0288023{col 70}{space 3} .5928073
{txt}{space 15} {c |}
{space 10}white {c |}
{space 1}White ID Only  {c |}{col 17}{res}{space 2}-.0517738{col 29}{space 2} .1785676{col 40}{space 1}   -0.29{col 49}{space 3}0.772{col 57}{space 4}-.4017598{col 70}{space 3} .2982122
{txt}{space 15} {c |}
{space 7}hispanic {c |}
{space 6}Hispanic  {c |}{col 17}{res}{space 2} .0412993{col 29}{space 2} .2359932{col 40}{space 1}    0.18{col 49}{space 3}0.861{col 57}{space 4}-.4212388{col 70}{space 3} .5038373
{txt}{space 15} {c |}
{space 8}college {c |}
College Degree  {c |}{col 17}{res}{space 2}-.2736568{col 29}{space 2} .1556545{col 40}{space 1}   -1.76{col 49}{space 3}0.079{col 57}{space 4} -.578734{col 70}{space 3} .0314204
{txt}{space 15} {c |}
{space 8}hhi_num {c |}
$25,000-$5~000  {c |}{col 17}{res}{space 2}  .065774{col 29}{space 2} .2062699{col 40}{space 1}    0.32{col 49}{space 3}0.750{col 57}{space 4}-.3385076{col 70}{space 3} .4700556
{txt}$50,000-$1~000  {c |}{col 17}{res}{space 2}-.0836644{col 29}{space 2} .2105237{col 40}{space 1}   -0.40{col 49}{space 3}0.691{col 57}{space 4}-.4962833{col 70}{space 3} .3289545
{txt}$100,000-$~000  {c |}{col 17}{res}{space 2}-.2154567{col 29}{space 2} .2543178{col 40}{space 1}   -0.85{col 49}{space 3}0.397{col 57}{space 4}-.7139104{col 70}{space 3} .2829969
{txt}More than $2..  {c |}{col 17}{res}{space 2} .6559334{col 29}{space 2}  .547027{col 40}{space 1}    1.20{col 49}{space 3}0.230{col 57}{space 4}-.4162199{col 70}{space 3} 1.728087
{txt}{space 15} {c |}
{space 11}pid7 {c |}
{space 6}Weak Dem  {c |}{col 17}{res}{space 2}-.0677395{col 29}{space 2}  .242029{col 40}{space 1}   -0.28{col 49}{space 3}0.780{col 57}{space 4}-.5421076{col 70}{space 3} .4066285
{txt}{space 6}Lean Dem  {c |}{col 17}{res}{space 2}-.6937208{col 29}{space 2} .2383379{col 40}{space 1}   -2.91{col 49}{space 3}0.004{col 57}{space 4}-1.160854{col 70}{space 3}-.2265872
{txt}{space 3}Independent  {c |}{col 17}{res}{space 2}-.9304475{col 29}{space 2} .2361812{col 40}{space 1}   -3.94{col 49}{space 3}0.000{col 57}{space 4}-1.393354{col 70}{space 3}-.4675409
{txt}{space 6}Lean GOP  {c |}{col 17}{res}{space 2}-.6347787{col 29}{space 2} .2947102{col 40}{space 1}   -2.15{col 49}{space 3}0.031{col 57}{space 4}  -1.2124{col 70}{space 3}-.0571574
{txt}{space 6}Weak GOP  {c |}{col 17}{res}{space 2} -.677671{col 29}{space 2} .2240802{col 40}{space 1}   -3.02{col 49}{space 3}0.002{col 57}{space 4} -1.11686{col 70}{space 3}-.2384818
{txt}{space 4}Strong GOP  {c |}{col 17}{res}{space 2}-.6992103{col 29}{space 2}  .255869{col 40}{space 1}   -2.73{col 49}{space 3}0.006{col 57}{space 4}-1.200704{col 70}{space 3}-.1977162
{txt}{space 15} {c |}
{space 7}employed {c |}
Not in Workf..  {c |}{col 17}{res}{space 2} .2782621{col 29}{space 2} .2655198{col 40}{space 1}    1.05{col 49}{space 3}0.295{col 57}{space 4}-.2421471{col 70}{space 3} .7986713
{txt}{space 5}Full time  {c |}{col 17}{res}{space 2} .0630876{col 29}{space 2} .2009513{col 40}{space 1}    0.31{col 49}{space 3}0.754{col 57}{space 4}-.3307696{col 70}{space 3} .4569449
{txt}{space 5}Part time  {c |}{col 17}{res}{space 2}-.0245315{col 29}{space 2}  .222448{col 40}{space 1}   -0.11{col 49}{space 3}0.912{col 57}{space 4}-.4605215{col 70}{space 3} .4114586
{txt}{space 15} {c |}
{space 10}order {c |}
{space 13}2  {c |}{col 17}{res}{space 2} -.014898{col 29}{space 2} .1706259{col 40}{space 1}   -0.09{col 49}{space 3}0.930{col 57}{space 4}-.3493187{col 70}{space 3} .3195226
{txt}{space 13}3  {c |}{col 17}{res}{space 2}-.7781252{col 29}{space 2} .1734836{col 40}{space 1}   -4.49{col 49}{space 3}0.000{col 57}{space 4}-1.118147{col 70}{space 3}-.4381035
{txt}{space 15} {c |}
{space 7}1.mobile {c |}{col 17}{res}{space 2} .0364779{col 29}{space 2} .1519183{col 40}{space 1}    0.24{col 49}{space 3}0.810{col 57}{space 4}-.2612766{col 70}{space 3} .3342323
{txt}1.attention_c~r {c |}{col 17}{res}{space 2}-1.280445{col 29}{space 2} .8686787{col 40}{space 1}   -1.47{col 49}{space 3}0.140{col 57}{space 4}-2.983024{col 70}{space 3} .4221341
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} 2.697433{col 29}{space 2} .9373514{col 40}{space 1}    2.88{col 49}{space 3}0.004{col 57}{space 4} .8602579{col 70}{space 3} 4.534608
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. eststo power_mod_2
{txt}
{com}. 
. coefplot power_mod_1, keep(*.humanitarian_treat_num3) base xline(0) ///
>         title("{c -(}it:OLS{c )-}") name(g_power_mod_1, replace)
{res}{txt}
{com}. 
. coefplot power_mod_2, keep(*.humanitarian_treat_num3) base xline(0) ///
>         title("{c -(}it:Logit{c )-}") name(g_power_mod_2, replace)        
{res}{txt}
{com}.         
. power twoproportions (0.3(0.1)0.6), n(1000) diff(.05(.025).125) graph(yline(.8) name(g_power,replace)) 
{res}{txt}
{com}. 
. gr combine g_power_mod_1 g_power_mod_2, rows(2) name(g_power_mod,replace)
{res}{txt}
{com}. 
. gr combine g_power_mod g_power, rows(1)
{res}{txt}
{com}. 
. ********************
. * Appendix Figure A1
. 
. gr export "${c -(}MyProject{c )-}01 Figure A1.pdf", replace
{txt}{p 0 4 2}
file {bf}
~/Dropbox/0001 Academic Projects/Ongoing/0155 Voting Abroad Purpose/Replication/Upload/01 Figure A1.pdf{rm}
saved as
PDF
format
{p_end}

{com}. 
. ********************************************************************************
. * Summary Plots and Tables
. ********************************************************************************
.         
. * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * 
. * Summary Table
. * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * 
. 
. /// Produces Table A2 in the appendix
> 
. /// Written before dtable, alas
> 
. tabulate gender3, generate(gender3_dum)

{txt}Three-Categ {c |}
 ory Gender {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
     Female {c |}{res}        743       49.04       49.04
{txt}       Male {c |}{res}        741       48.91       97.95
{txt}Alternative {c |}{res}         31        2.05      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,515      100.00
{txt}
{com}. lab var gender3_dum1 "Gender: Female"
{txt}
{com}. lab var gender3_dum2 "Gender: Male"
{txt}
{com}. lab var gender3_dum3 "Gender: Alternative"
{txt}
{com}. 
. tabulate employed, generate(employed_dum)

      {txt}Employment {c |}
          Status {c |}      Freq.     Percent        Cum.
{hline 17}{c +}{hline 35}
      Unemployed {c |}{res}        255       17.47       17.47
{txt}Not in Workforce {c |}{res}        184       12.60       30.07
{txt}       Full time {c |}{res}        708       48.49       78.56
{txt}       Part time {c |}{res}        313       21.44      100.00
{txt}{hline 17}{c +}{hline 35}
           Total {c |}{res}      1,460      100.00
{txt}
{com}. lab var employed_dum1 "Employment: Unemployed"
{txt}
{com}. lab var employed_dum2 "Employment: Not in Workforce"
{txt}
{com}. lab var employed_dum3 "Employment: Full Time"
{txt}
{com}. lab var employed_dum4 "Employment: Part Time"
{txt}
{com}. 
. tabulate pid3, generate(pid3_dum)

   {txt}Party ID {c |}
  (3-value) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
   Democrat {c |}{res}        545       36.60       36.60
{txt}Independent {c |}{res}        544       36.53       73.14
{txt} Republican {c |}{res}        400       26.86      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,489      100.00
{txt}
{com}. lab var pid3_dum1 "Party: Democrat"
{txt}
{com}. lab var pid3_dum2 "Party: Independent"
{txt}
{com}. lab var pid3_dum3 "Party: Republican"
{txt}
{com}. 
. lab var age "Age in Years"
{txt}
{com}.  
. estpost summarize age gender3_dum* pid3_dum* college white hispanic employed_dum* christian

{txt}{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:e(count)}{space 1}{space 1}{ralign 9:e(sum_w)}{space 1}{space 1}{ralign 9:e(mean)}{space 1}{space 1}{ralign 9:e(Var)}{space 1}{space 1}{ralign 9:e(sd)}{space 1}{space 1}{ralign 9:e(min)}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:age}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1515}}}{space 1}{space 1}{ralign 9:{res:{sf:     1515}}}{space 1}{space 1}{ralign 9:{res:{sf: 41.45479}}}{space 1}{space 1}{ralign 9:{res:{sf: 214.5625}}}{space 1}{space 1}{ralign 9:{res:{sf: 14.64795}}}{space 1}{space 1}{ralign 9:{res:{sf:       18}}}{space 1}
{space 0}{space 0}{ralign 12:gender3_dum1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1515}}}{space 1}{space 1}{ralign 9:{res:{sf:     1515}}}{space 1}{space 1}{ralign 9:{res:{sf:  .490429}}}{space 1}{space 1}{ralign 9:{res:{sf: .2500735}}}{space 1}{space 1}{ralign 9:{res:{sf: .5000735}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}
{space 0}{space 0}{ralign 12:gender3_dum2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1515}}}{space 1}{space 1}{ralign 9:{res:{sf:     1515}}}{space 1}{space 1}{ralign 9:{res:{sf: .4891089}}}{space 1}{space 1}{ralign 9:{res:{sf: .2500464}}}{space 1}{space 1}{ralign 9:{res:{sf: .5000464}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}
{space 0}{space 0}{ralign 12:gender3_dum3}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1515}}}{space 1}{space 1}{ralign 9:{res:{sf:     1515}}}{space 1}{space 1}{ralign 9:{res:{sf:  .020462}}}{space 1}{space 1}{ralign 9:{res:{sf: .0200566}}}{space 1}{space 1}{ralign 9:{res:{sf: .1416213}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}
{space 0}{space 0}{ralign 12:pid3_dum1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1489}}}{space 1}{space 1}{ralign 9:{res:{sf:     1489}}}{space 1}{space 1}{ralign 9:{res:{sf: .3660175}}}{space 1}{space 1}{ralign 9:{res:{sf: .2322046}}}{space 1}{space 1}{ralign 9:{res:{sf: .4818762}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}
{space 0}{space 0}{ralign 12:pid3_dum2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1489}}}{space 1}{space 1}{ralign 9:{res:{sf:     1489}}}{space 1}{space 1}{ralign 9:{res:{sf: .3653459}}}{space 1}{space 1}{ralign 9:{res:{sf: .2320241}}}{space 1}{space 1}{ralign 9:{res:{sf: .4816888}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}
{space 0}{space 0}{ralign 12:pid3_dum3}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1489}}}{space 1}{space 1}{ralign 9:{res:{sf:     1489}}}{space 1}{space 1}{ralign 9:{res:{sf: .2686367}}}{space 1}{space 1}{ralign 9:{res:{sf:  .196603}}}{space 1}{space 1}{ralign 9:{res:{sf: .4433994}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}
{space 0}{space 0}{ralign 12:college}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1515}}}{space 1}{space 1}{ralign 9:{res:{sf:     1515}}}{space 1}{space 1}{ralign 9:{res:{sf: .3927393}}}{space 1}{space 1}{ralign 9:{res:{sf: .2386527}}}{space 1}{space 1}{ralign 9:{res:{sf: .4885209}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}
{space 0}{space 0}{ralign 12:white}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1463}}}{space 1}{space 1}{ralign 9:{res:{sf:     1463}}}{space 1}{space 1}{ralign 9:{res:{sf: .7819549}}}{space 1}{space 1}{ralign 9:{res:{sf: .1706181}}}{space 1}{space 1}{ralign 9:{res:{sf: .4130594}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}
{space 0}{space 0}{ralign 12:hispanic}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1515}}}{space 1}{space 1}{ralign 9:{res:{sf:     1515}}}{space 1}{space 1}{ralign 9:{res:{sf: .1227723}}}{space 1}{space 1}{ralign 9:{res:{sf: .1077704}}}{space 1}{space 1}{ralign 9:{res:{sf:  .328284}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}
{space 0}{space 0}{ralign 12:employed_d~1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1460}}}{space 1}{space 1}{ralign 9:{res:{sf:     1460}}}{space 1}{space 1}{ralign 9:{res:{sf: .1746575}}}{space 1}{space 1}{ralign 9:{res:{sf: .1442511}}}{space 1}{space 1}{ralign 9:{res:{sf:  .379804}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}
{space 0}{space 0}{ralign 12:employed_d~2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1460}}}{space 1}{space 1}{ralign 9:{res:{sf:     1460}}}{space 1}{space 1}{ralign 9:{res:{sf: .1260274}}}{space 1}{space 1}{ralign 9:{res:{sf:   .11022}}}{space 1}{space 1}{ralign 9:{res:{sf:  .331994}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}
{space 0}{space 0}{ralign 12:employed_d~3}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1460}}}{space 1}{space 1}{ralign 9:{res:{sf:     1460}}}{space 1}{space 1}{ralign 9:{res:{sf: .4849315}}}{space 1}{space 1}{ralign 9:{res:{sf: .2499441}}}{space 1}{space 1}{ralign 9:{res:{sf: .4999441}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}
{space 0}{space 0}{ralign 12:employed_d~4}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1460}}}{space 1}{space 1}{ralign 9:{res:{sf:     1460}}}{space 1}{space 1}{ralign 9:{res:{sf: .2143836}}}{space 1}{space 1}{ralign 9:{res:{sf: .1685387}}}{space 1}{space 1}{ralign 9:{res:{sf: .4105346}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}
{space 0}{space 0}{ralign 12:christian}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1399}}}{space 1}{space 1}{ralign 9:{res:{sf:     1399}}}{space 1}{space 1}{ralign 9:{res:{sf: .4781987}}}{space 1}{space 1}{ralign 9:{res:{sf: .2497032}}}{space 1}{space 1}{ralign 9:{res:{sf: .4997031}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}

{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:e(max)}{space 1}{space 1}{ralign 9:e(sum)}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:age}{space 1}{c |}{space 1}{ralign 9:{res:{sf:       93}}}{space 1}{space 1}{ralign 9:{res:{sf:    62804}}}{space 1}
{space 0}{space 0}{ralign 12:gender3_dum1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      743}}}{space 1}
{space 0}{space 0}{ralign 12:gender3_dum2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      741}}}{space 1}
{space 0}{space 0}{ralign 12:gender3_dum3}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:       31}}}{space 1}
{space 0}{space 0}{ralign 12:pid3_dum1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      545}}}{space 1}
{space 0}{space 0}{ralign 12:pid3_dum2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      544}}}{space 1}
{space 0}{space 0}{ralign 12:pid3_dum3}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      400}}}{space 1}
{space 0}{space 0}{ralign 12:college}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      595}}}{space 1}
{space 0}{space 0}{ralign 12:white}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:     1144}}}{space 1}
{space 0}{space 0}{ralign 12:hispanic}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      186}}}{space 1}
{space 0}{space 0}{ralign 12:employed_d~1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      255}}}{space 1}
{space 0}{space 0}{ralign 12:employed_d~2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      184}}}{space 1}
{space 0}{space 0}{ralign 12:employed_d~3}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      708}}}{space 1}
{space 0}{space 0}{ralign 12:employed_d~4}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      313}}}{space 1}
{space 0}{space 0}{ralign 12:christian}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      669}}}{space 1}

{com}. est sto summarytable
{txt}
{com}.         
. ********************
. * Appendix Table A2
. 
. esttab summarytable using "${c -(}MyProject{c )-}AppendixTableA2.tex", replace ///
>     cells("count(fmt(a2)) mean sd min max") label                                                               ///
>     title("Humanitarian Experiment Descriptive Statistics""\label{c -(}tab:humanitariansummary{c )-}") nonumber noobs 
{res}{txt}(output written to {browse  `"~/Dropbox/0001 Academic Projects/Ongoing/0155 Voting Abroad Purpose/Replication/Upload/AppendixTableA2.tex"'})

{com}.         
. 
. * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * 
. * Balance Table 
. * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * 
. 
. gen white_only = 0
{txt}
{com}. replace white_only = 1 if white == 1 & hispanic == 0
{txt}(1,030 real changes made)

{com}. lab var white_only "White Non-Hispanic"
{txt}
{com}. 
. lab var attention_color "Attentiveness"
{txt}
{com}. 
. lab var age "Age in Years"
{txt}
{com}. 
.         
. ********************
. * Appendix Table A3
. 
. dtable age female pid3_dum* college white_only hispanic christian attention duration ///
>         , by(humanitarian_treat_num3) nformat(%8.2g)                                                            ///
>         export("${c -(}MyProject{c )-}AppendixTableA3.tex", replace tableonly)                    ///
>         title("Humanitarian Experiment \label{c -(}tab:humanitarianBalance{c )-}")        
{res}
{smcl}
{reset}{...}
{p}Humanitarian Experiment \label{tab:humanitarianBalance}{p_end}
{hline 28}{c -}{hline 9}{c -}{hline 10}{c -}{hline 10}{c -}{hline 11}
{space 28} {space 11}Humanitarian Treatment{space 10}
{space 28} {space 1}Interest {space 1}Christian {space 2}Muslim{space 2} {space 3}Total{space 3}
{hline 28}{c -}{hline 9}{c -}{hline 10}{c -}{hline 10}{c -}{hline 11}
N{space 27} {result:477 (31%)} {space 1}{result:533 (35%)} {space 1}{result:505 (33%)} {result:1515 (100%)}
Age in Years{space 16} {space 2}{result:41 (15)} {space 3}{result:42 (15)} {space 3}{result:42 (14)} {space 4}{result:41 (15)}
Female ID{space 19} {space 1}{result:.53 (.5)} {space 2}{result:.47 (.5)} {space 2}{result:.51 (.5)} {space 4}{result:.5 (.5)}
Party: Democrat{space 13} {result:.39 (.49)} {space 1}{result:.36 (.48)} {space 1}{result:.35 (.48)} {space 2}{result:.37 (.48)}
Party: Independent{space 10} {result:.35 (.48)} {space 1}{result:.38 (.49)} {space 1}{result:.37 (.48)} {space 2}{result:.37 (.48)}
Party: Republican{space 11} {result:.26 (.44)} {space 1}{result:.26 (.44)} {space 1}{result:.28 (.45)} {space 2}{result:.27 (.44)}
Has a college degree{space 8} {space 1}{result:.44 (.5)} {space 1}{result:.36 (.48)} {space 1}{result:.38 (.49)} {space 2}{result:.39 (.49)}
White Non-Hispanic{space 10} {result:.65 (.48)} {space 1}{result:.71 (.46)} {space 1}{result:.68 (.47)} {space 2}{result:.68 (.47)}
Hispanic ID{space 17} {result:.14 (.35)} {space 1}{result:.13 (.33)} {space 1}{result:.099 (.3)} {space 2}{result:.12 (.33)}
Christian Religious Identity {space 1}{result:.46 (.5)} {space 2}{result:.49 (.5)} {space 2}{result:.48 (.5)} {space 3}{result:.48 (.5)}
Attentiveness{space 15} {space 1}{result:1 (.065)} {result:.99 (.096)} {result:.99 (.089)} {space 1}{result:.99 (.085)}
Duration{space 20} {result:815 (755)} {space 1}{result:847 (891)} {space 1}{result:848 (853)} {space 2}{result:837 (837)}
{hline 28}{c -}{hline 9}{c -}{hline 10}{c -}{hline 10}{c -}{hline 11}
{res}{txt}{p 0 1 2}
(collection {res:DTable} exported to file {browse "/Users/rpm47/Dropbox/0001 Academic Projects/Ongoing/0155 Voting Abroad Purpose/Replication/Upload/~/Dropbox/0001 Academic Projects/Ongoing/0155 Voting Abroad Purpose/Replication/Upload/AppendixTableA3.tex":~/Dropbox/0001 Academic Projects/Ongoing/0155 Voting Abroad Purpose/Replication/Upload/AppendixTableA3.tex})
{p_end}

{com}.         
.         
. ********************************************************************************
. * Support for Intervention by Objective Type (H1)
. ********************************************************************************
. 
. * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *
. * Running Models
. * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *
. 
. *******************
. * Ologit
. *******************
. 
. local DV human_main_DV
{txt}
{com}. local DValt human_main_DV2
{txt}
{com}. 
. * Base Models (Treatments Only)
. 
. quietly ologit `DV' i.humanitarian_treat_num
{txt}
{com}. eststo human_m0_b0_olog, title("Base, Ologit")
{txt}
{com}. local n1 = e(N)
{txt}
{com}. quietly margins, post
{txt}
{com}. est sto human_m0_b0_olog_fx
{txt}
{com}. 
. quietly ologit `DValt' i.humanitarian_treat_num
{txt}
{com}. eststo human_m0_b0_olog2, title("Base, Ologit Alt")
{txt}
{com}. local n1alt = e(N)
{txt}
{com}. quietly margins, post
{txt}
{com}. est sto human_m0_b0_olog2_fx
{txt}
{com}. 
. * Adjused Models (Include Covariates)
. 
. quietly ologit `DV'                                                                                                     ///
>                 i.humanitarian_treat_num                                                                                                ///
>                 `demographics' `technical'
{txt}
{com}. eststo human_m1_olog, title("Adj, Ologit")
{txt}
{com}. local n2 = e(N)
{txt}
{com}. quietly margins, atmeans post
{txt}
{com}. est sto human_m1_olog_fx
{txt}
{com}. 
. quietly ologit `DValt'                                                                                                  ///
>                 i.humanitarian_treat_num                                                                                                ///
>                 `demographics' `technical'
{txt}
{com}. eststo human_m1_olog2, title("Adj, Ologit Alt")
{txt}
{com}. local n2alt = e(N)
{txt}
{com}. quietly margins, atmeans post
{txt}
{com}. est sto human_m1_olog2_fx
{txt}
{com}. 
. ********************
. * Appendix Table A4
. 
. esttab human_m0_b0_olog human_m0_b0_olog2 human_m1_olog human_m1_olog2                  ///
>         using "${c -(}MyProject{c )-}AppendixTableA4.tex"                 ///
>         ,       replace label title("Humanitarian Experiment, Ologit""\label{c -(}tab:humanitarianmainpaper{c )-}")                                       ///
>         mtitles("Base" "Base Alt DV" "Adj" "Adj Alt DV")                                                        ///
>         longtable nobase eqlabels(none) drop(cut*)
{res}{txt}(output written to {browse  `"~/Dropbox/0001 Academic Projects/Ongoing/0155 Voting Abroad Purpose/Replication/Upload/AppendixTableA4.tex"'})

{com}. 
. 
. * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * 
. * Descriptive Plots
. * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * 
. 
. * Produce Figure 1 A)
. 
. catplot  ,                                                                                                                                              ///
>         over(human_main_DV_trichot, label(labsize(*.65)))                                                       ///
>         over(humanitarian_treat_num3,label(labsize(2)))                                                         ///
>         asyvars stack                                                                                                                           ///
>         bar(1, bcolor(ebg)) bar(2, bcolor(dimgray))                                                             ///
>         horizontal percent(humanitarian_treat_num3)                                                             ///
>         ytitle("Percent")                                                                                                                       ///
>         blabel(bar,format(%4.1f) size(2) position(center))                                                      ///
>         legend(position(12) rows(1))                                                                                            ///
>         title("{c -(}bf:A) Support for Intervention by Treatment{c )-}")                                          ///
>         note("Control N = 477; Christian N = 533; , Muslim N = 505", size(vsmall))      ///
>         name(grhumanitarian_3way,replace)               
{res}{txt}
{com}. 
. * Produce Figure 1 B)
. 
. local model1 human_m0_b0_olog
{txt}
{com}. local model2 human_m0_b0_olog2
{txt}
{com}. local model3 human_m1_olog
{txt}
{com}. local model4 human_m1_olog2
{txt}
{com}. 
. local model1title "Base"
{txt}
{com}. local model2title "Base, Alt DV"
{txt}
{com}. local model3title "Adj"
{txt}
{com}. local model4title "Adj, Alt DV"
{txt}
{com}. 
. local gtitle g_h1_ologit
{txt}
{com}. 
. coefplot        (`model1', label(`model1title'))                                                                        ///
>                         (`model2', label(`model2title'))                                                                        ///
>                         (`model3', label(`model3title'))                                                                        ///
>                         (`model4', label(`model4title'))                                                                        ///
>                         , title("{c -(}bf: B) Effects of Treatment on Intervention Support{c )-}")        ///
>                         xtitle("{c -(}it: Ordinal Logistic Coefficients{c )-}")                                           ///
>                         keep(*.human*) base                                                                                                     ///
>                         name(`gtitle',replace)  xline(0)                                                                        ///
>                         legend(ring(0) pos(1) rows(42) size(small))                                             ///
>                         ylab(,labsize(vsmall))                                                                                          ///
>                         note("`model1title' N = `n1', `model2title' N = `n1alt', `model3title' N = `n2', `model4title' N = `n2alt'"                                                                                             ///
>                         "Full specification for Adjusted includes demographic and technical covariates. Alt DVs omit 'neither' categories.", size(vsmall))
{res}{txt}
{com}. 
. 
. * Produce Figure 1 C)
.                         
. est restore human_m1_olog
{txt}(results {stata estimates replay human_m1_olog:human_m1_olog} are active now)

{com}.  margins , atmeans dydx(humanitarian_treat_num) post
{res}
{txt}{col 1}Conditional marginal effects{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:1,364}
{txt}{col 1}Model VCE: {res:OIM}

{txt}{p2colset 1 12 12 2}{...}
{p2col:dy/dx wrt:}{res:1.humanitarian_treat_num}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 13 13 2}{...}
{p2col:{txt:1._predict:}}{res:Pr(human_main_DV==0), predict(pr outcome(0))}{p_end}
{p2col:{txt:2._predict:}}{res:Pr(human_main_DV==1), predict(pr outcome(1))}{p_end}
{p2col:{txt:3._predict:}}{res:Pr(human_main_DV==2), predict(pr outcome(2))}{p_end}
{p2col:{txt:4._predict:}}{res:Pr(human_main_DV==3), predict(pr outcome(3))}{p_end}
{p2col:{txt:5._predict:}}{res:Pr(human_main_DV==4), predict(pr outcome(4))}{p_end}
{p2col:{txt:6._predict:}}{res:Pr(human_main_DV==5), predict(pr outcome(5))}{p_end}
{p2col:{txt:7._predict:}}{res:Pr(human_main_DV==6), predict(pr outcome(6))}{p_end}
{p2colreset}{...}

{lalign 4:At: }{space 0}{lalign 24:0.humanitarian_treat_num} = {res:{ralign 8:.3152493}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:1.humanitarian_treat_num} = {res:{ralign 8:.6847507}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:age} = {res:{ralign 8:42.05718}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:0.female} = {res:{ralign 8:.5021994}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:1.female} = {res:{ralign 8:.4978006}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:0.white} = {res:{ralign 8:.2162757}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:1.white} = {res:{ralign 8:.7837243}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:0.hispanic} = {res:{ralign 8:.8958944}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:1.hispanic} = {res:{ralign 8:.1041056}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:0.college} = {res:{ralign 8:.5960411}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:1.college} = {res:{ralign 8:.4039589}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:0.hhi_num} = {res:{ralign 8:.1788856}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:1.hhi_num} = {res:{ralign 8:.2961877}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:2.hhi_num} = {res:{ralign 8:.3482405}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:3.hhi_num} = {res:{ralign 8:.1539589}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:4.hhi_num} = {res:{ralign 8:.0227273}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:0.pid7} = {res:{ralign 8:.2162757}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:1.pid7} = {res:{ralign 8:.1480938}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:2.pid7} = {res:{ralign 8:.1231672}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:3.pid7} = {res:{ralign 8:.1517595}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:4.pid7} = {res:{ralign 8:.0755132}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:5.pid7} = {res:{ralign 8:.164956}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:6.pid7} = {res:{ralign 8:.1202346}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:0.employed} = {res:{ralign 8:.1678886}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:1.employed} = {res:{ralign 8:.1246334}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:2.employed} = {res:{ralign 8:.4941349}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:3.employed} = {res:{ralign 8:.2133431}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:1.order} = {res:{ralign 8:.3167155}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:2.order} = {res:{ralign 8:.3504399}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:3.order} = {res:{ralign 8:.3328446}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:0.mobile} = {res:{ralign 8:.691349}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:1.mobile} = {res:{ralign 8:.308651}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:0.attention_color} = {res:{ralign 8:.0073314}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:1.attention_color} = {res:{ralign 8:.9926686}} {txt:(mean)}

{res}{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29} Delta-method
{col 17}{c |}      dy/dx{col 29}   std. err.{col 41}      z{col 49}   P>|z|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0.humanitaria~m{col 17}{txt}{c |}  (base outcome)
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.humanitaria~m {txt}{c |}
{space 7}_predict {c |}
{space 13}1  {c |}{col 17}{res}{space 2}-.0798212{col 29}{space 2} .0152516{col 40}{space 1}   -5.23{col 49}{space 3}0.000{col 57}{space 4}-.1097138{col 70}{space 3}-.0499287
{txt}{space 13}2  {c |}{col 17}{res}{space 2}-.0443945{col 29}{space 2} .0081742{col 40}{space 1}   -5.43{col 49}{space 3}0.000{col 57}{space 4}-.0604157{col 70}{space 3}-.0283733
{txt}{space 13}3  {c |}{col 17}{res}{space 2}-.0186657{col 29}{space 2} .0034896{col 40}{space 1}   -5.35{col 49}{space 3}0.000{col 57}{space 4}-.0255051{col 70}{space 3}-.0118262
{txt}{space 13}4  {c |}{col 17}{res}{space 2}-.0019791{col 29}{space 2} .0024057{col 40}{space 1}   -0.82{col 49}{space 3}0.411{col 57}{space 4}-.0066942{col 70}{space 3}  .002736
{txt}{space 13}5  {c |}{col 17}{res}{space 2} .0293588{col 29}{space 2} .0062797{col 40}{space 1}    4.68{col 49}{space 3}0.000{col 57}{space 4} .0170507{col 70}{space 3} .0416668
{txt}{space 13}6  {c |}{col 17}{res}{space 2} .0591946{col 29}{space 2} .0105668{col 40}{space 1}    5.60{col 49}{space 3}0.000{col 57}{space 4}  .038484{col 70}{space 3} .0799052
{txt}{space 13}7  {c |}{col 17}{res}{space 2} .0563072{col 29}{space 2} .0095637{col 40}{space 1}    5.89{col 49}{space 3}0.000{col 57}{space 4} .0375626{col 70}{space 3} .0750517
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 81}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. est sto human_m1_olog_dydx,title("Covariates")
{txt}
{com}. 
. est restore human_m0_b0_olog
{txt}(results {stata estimates replay human_m0_b0_olog:human_m0_b0_olog} are active now)

{com}.  margins , atmeans dydx(humanitarian_treat_num) post
{res}
{txt}{col 1}Conditional marginal effects{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:1,515}
{txt}{col 1}Model VCE: {res:OIM}

{txt}{p2colset 1 12 12 2}{...}
{p2col:dy/dx wrt:}{res:1.humanitarian_treat_num}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 13 13 2}{...}
{p2col:{txt:1._predict:}}{res:Pr(human_main_DV==0), predict(pr outcome(0))}{p_end}
{p2col:{txt:2._predict:}}{res:Pr(human_main_DV==1), predict(pr outcome(1))}{p_end}
{p2col:{txt:3._predict:}}{res:Pr(human_main_DV==2), predict(pr outcome(2))}{p_end}
{p2col:{txt:4._predict:}}{res:Pr(human_main_DV==3), predict(pr outcome(3))}{p_end}
{p2col:{txt:5._predict:}}{res:Pr(human_main_DV==4), predict(pr outcome(4))}{p_end}
{p2col:{txt:6._predict:}}{res:Pr(human_main_DV==5), predict(pr outcome(5))}{p_end}
{p2col:{txt:7._predict:}}{res:Pr(human_main_DV==6), predict(pr outcome(6))}{p_end}
{p2colreset}{...}

{lalign 4:At: }{space 0}{lalign 24:0.humanitarian_treat_num} = {res:{ralign 8:.3148515}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:1.humanitarian_treat_num} = {res:{ralign 8:.6851485}} {txt:(mean)}

{res}{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29} Delta-method
{col 17}{c |}      dy/dx{col 29}   std. err.{col 41}      z{col 49}   P>|z|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0.humanitaria~m{col 17}{txt}{c |}  (base outcome)
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.humanitaria~m {txt}{c |}
{space 7}_predict {c |}
{space 13}1  {c |}{col 17}{res}{space 2}-.0763528{col 29}{space 2} .0147649{col 40}{space 1}   -5.17{col 49}{space 3}0.000{col 57}{space 4}-.1052914{col 70}{space 3}-.0474143
{txt}{space 13}2  {c |}{col 17}{res}{space 2}-.0376606{col 29}{space 2} .0070757{col 40}{space 1}   -5.32{col 49}{space 3}0.000{col 57}{space 4}-.0515287{col 70}{space 3}-.0237926
{txt}{space 13}3  {c |}{col 17}{res}{space 2} -.016409{col 29}{space 2}    .0031{col 40}{space 1}   -5.29{col 49}{space 3}0.000{col 57}{space 4}-.0224848{col 70}{space 3}-.0103332
{txt}{space 13}4  {c |}{col 17}{res}{space 2}-.0014706{col 29}{space 2} .0019727{col 40}{space 1}   -0.75{col 49}{space 3}0.456{col 57}{space 4}-.0053371{col 70}{space 3} .0023959
{txt}{space 13}5  {c |}{col 17}{res}{space 2} .0244294{col 29}{space 2} .0053145{col 40}{space 1}    4.60{col 49}{space 3}0.000{col 57}{space 4} .0140131{col 70}{space 3} .0348457
{txt}{space 13}6  {c |}{col 17}{res}{space 2} .0510836{col 29}{space 2} .0093988{col 40}{space 1}    5.44{col 49}{space 3}0.000{col 57}{space 4} .0326622{col 70}{space 3}  .069505
{txt}{space 13}7  {c |}{col 17}{res}{space 2}   .05638{col 29}{space 2} .0097995{col 40}{space 1}    5.75{col 49}{space 3}0.000{col 57}{space 4} .0371734{col 70}{space 3} .0755866
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 81}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. est sto human_m0_olog_dydx,title("Base")
{txt}
{com}. 
. mylabels -10(5)10, myscale(@/100) local(myla)
{res}{p}                -.1 "-10"                -.05 "-5"                   0 "0"                 .05 "5"                  .1 "10" 
{txt}
{smcl}
{com}. 
. coefplot (human_m1_olog_dydx, label(Base))                                                                              ///
>                         (human_m0_olog_dydx, label(Covariates))                                                         ///
>         , recast(bar) vertical barw(0.25)                                                                                       ///
>         ciopts(recast(rcap) color(gs8)) citop                                                                           ///
>         xlab(   1 `""Strongly" "disapprove""' 2 "Disapprove"                                            ///
>                         3 `""Somewhat" "disapprove""'   4 "Neither" 5 `""Somewhat" "approve""' ///
>                         6 "Approve" 7 `""Strongly" "approve""'                                                          ///
>                 , labsize(vsmall))                                                                                                              ///
>         title("{c -(}bf: C) Differences in Predictions by Treatment, Humanitarian - Interest{c )-}", size(medsmall))      ///
>         note("Base model includes no covariates; Covariates includes technical and demographic variables.",size(small))         ///
>         yline(0) ylab(`myla',labsize(vsmall))                                                                   ///
>         name(gph_dydx_ologit, replace)                                                                                  ///
>         legend(rows(1) ring(2) pos(12)) 
{res}{txt}
{com}.         
. 
. ********************
. * Main Paper Figure 1
. 
. gr combine grhumanitarian_3way g_h1_ologit gph_dydx_ologit                                              ///
>         , rows(3)                                                                                                                                       ///
>         name(exprop_h1_main_gph,replace)
{res}{txt}
{com}. 
.         graph display, ysize(9) xsize(6)
{res}{txt}
{com}. 
. gr export "${c -(}MyProject{c )-}00 Main Figure 1.pdf", replace
{txt}{p 0 4 2}
file {bf}
~/Dropbox/0001 Academic Projects/Ongoing/0155 Voting Abroad Purpose/Replication/Upload/00 Main Figure 1.pdf{rm}
saved as
PDF
format
{p_end}

{com}. 
. 
. 
. * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * 
. * Subgroups
. * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * 
. 
. * * * * * * * Gender
. 
. local model1title "Female"
{txt}
{com}. local model2title "Male"
{txt}
{com}. local model1 human_m1_olog_female
{txt}
{com}. local model2 human_m1_olog_male
{txt}
{com}. local title Gender
{txt}
{com}. local gtitle gph_`title'
{txt}
{com}. 
. local DV human_main_DV
{txt}
{com}. local DValt human_main_DV2
{txt}
{com}. 
. * Base
. quietly ologit `DV' i.humanitarian_treat_num
{txt}
{com}. 
. 
. quietly ologit `DV' i.humanitarian_treat_num                                                                    ///
>                 age i.white i.hispanic i.college i.hhi_num i.pid7 i.employed                    ///
>                 `technical'                                                                                                                             ///
>                 if female == 1
{txt}
{com}. eststo `model1', title(`model1title')
{txt}
{com}. local n1 = e(N)
{txt}
{com}. 
. quietly ologit `DV' i.humanitarian_treat_num                                                                    ///
>                 age i.white i.hispanic i.college i.hhi_num i.pid7 i.employed                    ///
>                 `technical'                                                                                                                             ///
>                 if female == 0
{txt}
{com}. eststo `model2', title(`model2title')
{txt}
{com}. local n2 = e(N)
{txt}
{com}. 
. coefplot        (`model1', label(`model1title'))                                                                        ///
>                         (`model2', label(`model2title')),                                                                       ///
>                         title("{c -(}it: `title'{c )-}")                                                                                          ///
>                         keep(*.humanitarian*) base                                                                                      ///
>                         levels(95 83 )  ciopts(lwidth(.75...) lcolor(*.4 *.9))                          ///     
>                         name(`gtitle',replace)  xline(0)                                                                        ///
>                         legend(ring(0) pos(9) rows(2) size(vsmall))                                                                             ///
>                         ylab(,labsize(tiny))                                                                                            ///
>                         note("`model1title' N = `n1', `model2title' N = `n2'", size(tiny))
{res}{txt}
{com}. 
. 
. * * * * * * * Education
. 
. local model1title "College"
{txt}
{com}. local model2title "No College"
{txt}
{com}. local model1 human_m1_olog_college
{txt}
{com}. local model2 human_m1_olog_nocollege
{txt}
{com}. local title Education
{txt}
{com}. local gtitle gph_`title'
{txt}
{com}. 
. quietly ologit `DV' i.humanitarian_treat_num                                                                    ///
>                 age i.female i.white i.hispanic i.hhi_num i.pid7 i.employed                             ///
>                 `technical'                                                                                                                             ///
>                 if college == 1
{txt}
{com}. eststo `model1', title(`model1title')
{txt}
{com}. local n1 = e(N)
{txt}
{com}. 
. quietly ologit `DV' i.humanitarian_treat_num                                                                    ///
>                 age i.female i.white i.hispanic  i.hhi_num i.pid7 i.employed                    ///
>                 `technical'                                                                                                                             ///
>                 if college == 0
{txt}
{com}. eststo `model2', title(`model2title')
{txt}
{com}. local n2 = e(N)
{txt}
{com}. 
. coefplot        (`model1', label(`model1title'))                                                                        ///
>                         (`model2', label(`model2title'))                                                                        ///
>                         , title("{c -(}it: `title'{c )-}")                                                                                        ///
>                         keep(*.humanitarian*) base                                                                                      ///
>                         levels(95 83 )  ciopts(lwidth(.75...) lcolor(*.4 *.9))                          ///     
>                         name(`gtitle',replace)  xline(0)                                                                        ///
>                         legend(ring(0) pos(10) rows(2) size(vsmall))                                                                            ///
>                         ylab(,labsize(tiny))                                                                                            ///
>                         note("`model1title' N = `n1', `model2title' N = `n2'", size(tiny))
{res}{txt}
{com}. 
. 
. 
. * * * * * * * Party ID
. 
. local model1title "Democrats"
{txt}
{com}. local model2title "Independents"
{txt}
{com}. local model3title "Republicans"
{txt}
{com}. 
. local model1 human_m1_olog_dem
{txt}
{com}. local model2 human_m1_olog_ind
{txt}
{com}. local model3 human_m1_olog_gop
{txt}
{com}. 
. local title Party
{txt}
{com}. local gtitle gph_`title'
{txt}
{com}. 
. quietly ologit `DV' i.humanitarian_treat_num                                                                    ///
>                 age i.female i.white i.hispanic i.college i.hhi_num i.employed                  ///
>                 `technical'                                                                                                                             ///
>                 if pid3alt == 0
{txt}
{com}. eststo `model1', title(`model1title')
{txt}
{com}. local n1 = e(N)
{txt}
{com}. 
. quietly ologit `DV' i.humanitarian_treat_num                                                                    ///
>                 age i.female i.white i.hispanic i.college i.hhi_num i.employed                  ///
>                 `technical'                                                                                                                             ///
>                 if pid3alt == 1
{txt}
{com}. eststo `model2', title(`model2title')
{txt}
{com}. 
. quietly ologit `DV' i.humanitarian_treat_num                                                                    ///
>                 age i.female i.white i.hispanic i.college i.hhi_num i.employed                  ///
>                 `technical'                                                                                                                             ///
>                 if pid3alt == 2
{txt}
{com}. eststo `model3', title(`model3title')
{txt}
{com}. local n3 = e(N)
{txt}
{com}. 
. 
. coefplot        (`model1', label(`model1title'))                                                                        ///
>                         (`model2', label(`model2title'))                                                                        ///
>                         (`model3', label(`model3title'))                                                                        ///
>                         , title("{c -(}it: `title'{c )-}")                                                                                        ///
>                         keep(*.humanitarian*) base                                                                                      ///
>                         levels(95 83 )  ciopts(lwidth(.75...) lcolor(*.4 *.9))                          ///     
>                         name(`gtitle',replace)  xline(0)                                                                        ///
>                         legend(ring(0) pos(9) rows(3) size(tiny))                                                                               ///
>                         ylab(,labsize(tiny))                                                                                            ///
>                         note("`model1title' N = `n1', `model2title' N = `n2', `model3title' N = `n3'", size(tiny))
{res}{txt}
{com}. 
. * * * * * * * Covid Exposure
. 
. local model1title "Exposed"
{txt}
{com}. local model2title "Not Exposed"
{txt}
{com}. local model1 human_m1_olog_covid
{txt}
{com}. local model2 human_m1_olog_novid
{txt}
{com}. local title Covid
{txt}
{com}. local gtitle gph_`title'
{txt}
{com}. 
. 
. quietly ologit `DV' i.humanitarian_treat_num                                                                    ///
>                 age i.female i.white i.college i.hispanic i.hhi_num i.pid7 i.employed   ///
>                 `technical'                                                                                                                             ///
>                 if covidexposure == 1
{txt}
{com}. eststo `model1', title(`model1title')
{txt}
{com}. local n1 = e(N)
{txt}
{com}. 
. quietly ologit `DV' i.humanitarian_treat_num                                                                    ///
>                 age i.female i.white i.college i.hispanic i.hhi_num i.pid7 i.employed   ///
>                 `technical'                                                                                                                             ///
>                 if covidexposure == 0
{txt}
{com}. eststo `model2', title(`model2title')
{txt}
{com}. local n2 = e(N)
{txt}
{com}. 
. 
. 
. coefplot        (`model1', label(`model1title'))                                                                        ///
>                         (`model2', label(`model2title'))                                                                        ///
>                         , title("{c -(}it: `title'{c )-}")                                                                                        ///
>                         keep(*.humanitarian*) base                                                                                      ///
>                         levels(95 83 )  ciopts(lwidth(.75...) lcolor(*.4 *.9))                          ///     
>                         name(`gtitle',replace)  xline(0)                                                                        ///
>                         legend(ring(0) pos(1) rows(2) size(vsmall))                                                                             ///
>                         ylab(,labsize(vsmall))                                                                                          ///
>                         note("`model1title' N = `n1', `model2title' N = `n2'", size(tiny))
{res}{txt}
{com}. 
. 
. 
. * * * * * * * * Employment
. 
. local model1title "Not Working"
{txt}
{com}. local model2title "Part Time"
{txt}
{com}. local model3title "Full-Time"
{txt}
{com}. 
. local model1 human_m1_olog_nojob
{txt}
{com}. local model2 human_m1_olog_pt
{txt}
{com}. local model3 human_m1_olog_ft
{txt}
{com}. 
. local title Employment
{txt}
{com}. local gtitle gph_`title'
{txt}
{com}. 
. 
. quietly ologit `DV' i.humanitarian_treat_num                                                                    ///
>                 age i.female i.white i.hispanic i.college i.hhi_num                                     ///
>                 `technical'                                                                                                                             ///
>                 if employed == 0 | employed == 1
{txt}
{com}. eststo `model1', title("Not in Workforce")
{txt}
{com}. local n1 = e(N)
{txt}
{com}. 
. quietly ologit `DV' i.humanitarian_treat_num                                                                    ///
>                 age i.female i.white i.hispanic i.college i.hhi_num                                     ///
>                 `technical'                                                                                                                             ///
>                 if employed == 3
{txt}
{com}. eststo `model2', title("Part Time")
{txt}
{com}. local n2 = e(N)
{txt}
{com}. 
. quietly ologit `DV' i.humanitarian_treat_num                                                                    ///
>                 age i.female i.white i.hispanic i.college i.hhi_num                                     ///
>                 `technical'                                                                                                                             ///
>                 if employed == 2
{txt}
{com}. eststo `model3', title("Full Time")
{txt}
{com}. local n3 = e(N)
{txt}
{com}. 
. 
. coefplot        (`model1', label(`model1title'))                                                                        ///
>                         (`model2', label(`model2title'))                                                                        ///
>                         (`model3', label(`model3title'))                                                                        ///
>                         , title("{c -(}it: `title'{c )-}")                                                                                        ///
>                         keep(*.humanitarian*) base                                                                                      ///
>                         levels(95 83 )  ciopts(lwidth(.75...) lcolor(*.4 *.9))                          ///     
>                         name(`gtitle',replace)  xline(0)                                                                        ///
>                         legend(ring(0) pos(9) rows(3) size(tiny))                                                                               ///
>                         ylab(,labsize(tiny))                                                                                            ///
>                         note("`model1title' N = `n1', `model2title' N = `n2', `model3title' N = `n3'", size(tiny))
{res}{txt}
{com}. 
. * * * * * * * * Russian intervention effectiveness
. 
. 
. local model1title "Ineffective"
{txt}
{com}. local model2title "Neither"
{txt}
{com}. local model3title "Effective"
{txt}
{com}. 
. local model1 human_m1_olog_ineff
{txt}
{com}. local model2 human_m1_olog_neither
{txt}
{com}. local model3 human_m1_olog_effect
{txt}
{com}. 
. local title Russia
{txt}
{com}. local gtitle gph_`title'
{txt}
{com}. 
. 
. quietly ologit `DV' i.humanitarian_treat_num                                                                    ///
>                 age i.female i.white i.hispanic i.college i.hhi_num i.pid7 i.employed   ///
>                 `technical'                                                                                                                             ///
>                 if russia_effect_3pt == 0
{txt}
{com}. eststo `model1', title(`model1title')
{txt}
{com}. local n1 = e(N)
{txt}
{com}. 
. quietly ologit `DV' i.humanitarian_treat_num                                                                    ///
>                 age i.female i.white i.hispanic i.college i.hhi_num i.pid7 i.employed   ///
>                 `technical'                                                                                                                             ///
>                 if russia_effect_3pt == 1
{txt}
{com}. eststo `model2', title(`model1title')
{txt}
{com}. local n2 = e(N)
{txt}
{com}. 
. quietly ologit `DV' i.humanitarian_treat_num                                                                    ///
>                 age i.female i.white i.hispanic i.college i.hhi_num i.pid7 i.employed   ///
>                 `technical'                                                                                                                             ///
>                 if russia_effect_3pt == 2
{txt}
{com}. eststo `model3', title(`model3title')
{txt}
{com}. local n3 = e(N)
{txt}
{com}. 
. 
. coefplot        (`model1', label(`model1title'))                                                                        ///
>                         (`model2', label(`model2title'))                                                                        ///
>                         (`model3', label(`model3title'))                                                                        ///
>                         , title("{c -(}it: `title'{c )-}")                                                                                        ///
>                         keep(*.humanitarian*) base                                                                                      ///
>                         levels(95 83 )  ciopts(lwidth(.75...) lcolor(*.4 *.9))                          ///     
>                         name(`gtitle',replace)  xline(0)                                                                        ///
>                         legend(ring(0) pos(2) rows(3) size(tiny) fcolor(%30))                                                                           ///
>                         ylab(,labsize(tiny))                                                                                            ///
>                         note("`model1title' N = `n1', `model2title' N = `n2', `model3title' N = `n3'", size(tiny))
{res}{txt}
{com}. 
. * * * * * * * * * * Hawkishness
. 
. * cut hawkishness into thirds
. 
. local model1title "Doves"
{txt}
{com}. local model2title "Middle"
{txt}
{com}. local model3title "Hawks"
{txt}
{com}. 
. local model1 human_m1_olog_dove
{txt}
{com}. local model2 human_m1_olog_mush
{txt}
{com}. local model3 human_m1_olog_hawk
{txt}
{com}. 
. local title Hawkishness
{txt}
{com}. local gtitle gph_`title'
{txt}
{com}. 
. 
. quietly ologit `DV' i.humanitarian_treat_num                                                                    ///
>                 age i.female i.white i.hispanic i.college i.hhi_num i.pid7 i.employed   ///
>                 `technical'                                                                                                                             ///
>                 if hawkishcat == 0
{txt}
{com}. eststo `model1', title(`model1title')
{txt}
{com}. local n1 = e(N)
{txt}
{com}. 
. quietly ologit `DV' i.humanitarian_treat_num                                                                    ///
>                 age i.female i.white i.hispanic i.college i.hhi_num i.pid7 i.employed   ///
>                 `technical'                                                                                                                             ///
>                 if hawkishcat == 1
{txt}
{com}. eststo `model2', title(`model2title')
{txt}
{com}. local n2 = e(N)
{txt}
{com}. 
. quietly ologit `DV' i.humanitarian_treat_num                                                                    ///
>                 age i.female i.white i.hispanic i.college i.hhi_num i.pid7 i.employed   ///
>                 `technical'                                                                                                                             ///
>                 if hawkishcat == 2
{txt}
{com}. eststo `model3', title(`model3title')
{txt}
{com}. local n3 = e(N)
{txt}
{com}. 
. 
. coefplot        (`model1', label(`model1title'))                                                                        ///
>                         (`model2', label(`model2title'))                                                                        ///
>                         (`model3', label(`model3title'))                                                                        ///
>                         , title("{c -(}it: `title'{c )-}")                                                                                        ///
>                         keep(*.humanitarian*) base                                                                                      ///
>                         levels(95 83 )  ciopts(lwidth(.75...) lcolor(*.4 *.9))                          ///     
>                         name(`gtitle',replace)  xline(0)                                                                        ///
>                         legend(ring(0) pos(9) rows(3) size(tiny))                                                                               ///
>                         ylab(,labsize(tiny))                                                                                            ///
>                         note("`model1title' N = `n1', `model2title' N = `n2', `model3title' N = `n3'", size(tiny))
{res}{txt}
{com}. 
. * * * * * * * * Order
. 
. local model1title "1"
{txt}
{com}. local model2title "2"
{txt}
{com}. local model3title "3"
{txt}
{com}. 
. local model1 human_m1_olog_1
{txt}
{com}. local model2 human_m1_olog_2
{txt}
{com}. local model3 human_m1_olog_3
{txt}
{com}. 
. local title Order
{txt}
{com}. local gtitle gph_`title'
{txt}
{com}. 
. quietly ologit `DV' i.humanitarian_treat_num                                                                    ///
>                 age i.female i.white i.hispanic i.college i.hhi_num i.pid7 i.employed   ///
>                 `technical'                                                                                                                             ///
>                 if order == 1
{txt}
{com}. eststo `model1', title(`model1title')
{txt}
{com}. local n1 = e(N)
{txt}
{com}. 
. quietly ologit `DV' i.humanitarian_treat_num                                                                    ///
>                 age i.female i.white i.hispanic i.college i.hhi_num i.pid7 i.employed   ///
>                 `technical'                                                                                                                             ///
>                 if order == 2
{txt}
{com}. eststo `model2', title(`model1title')
{txt}
{com}. local n2 = e(N)
{txt}
{com}. 
. quietly ologit `DV' i.humanitarian_treat_num                                                                    ///
>                 age i.female i.white i.hispanic i.college i.hhi_num i.pid7 i.employed   ///
>                 `technical'                                                                                                                             ///
>                 if order == 3
{txt}
{com}. eststo `model3', title(`model3title')
{txt}
{com}. local n3 = e(N)
{txt}
{com}. 
. 
. coefplot        (`model1', label(`model1title'))                                                                        ///
>                         (`model2', label(`model2title'))                                                                        ///
>                         (`model3', label(`model3title'))                                                                        ///
>                         , title("{c -(}it: `title'{c )-}")                                                                                        ///
>                         keep(*humanitarian*) base                                                                                                       ///
>                         levels(95 83 )  ciopts(lwidth(.75...) lcolor(*.4 *.9))                          ///     
>                         name(`gtitle',replace)  xline(0)                                                                        ///
>                         legend(ring(0) pos(9) rows(3) size(tiny))                                                                               ///
>                         ylab(,labsize(vsmall))                                                                                          ///
>                         note("`model1title' N = `n1', `model2title' N = `n2', `model3title' N = `n3'", size(tiny))
{res}{txt}
{com}.                                                 
. 
. 
. ********************
. * Appendix Figure A2
. 
. gr combine gph_Education gph_Hawkishness gph_Employment gph_Russia gph_Covid    ///
>                 gph_Gender gph_Party gph_Order                                                                                                  ///
>                 , rows(4) title("{c -(}bf:Results by Subgroup{c )-}")                                                             ///
>                 subtitle("{c -(}it: Ordinal Logistic Regression Coefficients{c )-}")                              ///
>                 note("95% confidence intervals in faint lines; 83% confidence intervals in dark. Full specification includes demographic and technical covariates.",size(tiny)) ///
>                 xcommon
{res}{txt}
{com}.                 
. gr display, ysize(9) xsize(7) 
{res}{txt}
{com}.                 
. gr export "${c -(}MyProject{c )-}01 Figure A2.pdf", replace
{txt}{p 0 4 2}
file {bf}
~/Dropbox/0001 Academic Projects/Ongoing/0155 Voting Abroad Purpose/Replication/Upload/01 Figure A2.pdf{rm}
saved as
PDF
format
{p_end}

{com}.                 
. 
. ********************************************************************************
. * Selective Support for Humanitarian Intervention (H1.1)
. ********************************************************************************
. 
. * In-Group/Out-Group Plot
. * Using models with covariates for presentation
. 
. 
. * All
. quietly ologit human_main_DV                                                                                                    ///
>                 i.humanitarian_treat_num3                                                                                               ///
>                 `demographics' `technical' if humanitarian_treat_num3 >=1
{txt}
{com}. eststo comparison_all, title("All Respondents, Ologit")
{txt}
{com}. local n2 = e(N)
{txt}
{com}. quietly margins, atmeans post
{txt}
{com}. est sto comparison_all_fx
{txt}
{com}. 
. * Christians Only
. quietly ologit human_main_DV                                                                                                                    ///
>                 i.humanitarian_treat_num3                                                                                               ///
>                 `demographics' `technical' if humanitarian_treat_num3 >=1 & christian == 1
{txt}
{com}. eststo comparison_xtian, title("Christians Only, Ologit")
{txt}
{com}. local n2_x = e(N)
{txt}
{com}. quietly margins, atmeans post
{txt}
{com}. est sto comparison_xtian_fx
{txt}
{com}. 
. 
. * Non-Christians Only
. quietly ologit human_main_DV                                                                                                                    ///
>                 i.humanitarian_treat_num3                                                                                               ///
>                 `demographics' `technical' if humanitarian_treat_num3 >=1 & christian == 0
{txt}
{com}. eststo comparison_notian, title("Non-Christians Only, Ologit")
{txt}
{com}. local n2_notx = e(N)
{txt}
{com}. quietly margins, atmeans post
{txt}
{com}. est sto comparison_notian_fx
{txt}
{com}. 
. /// Produces Figure 2 A)
> 
. coefplot        (comparison_all, label(All))                                                                                    ///
>                         (comparison_xtian, label(Christians))                                                                   ///
>                         (comparison_notian, label("Not Christians"))                                                    ///
>                         , title("{c -(}bf: A) Effects of Victim Identity by Respondent Identity{c )-}")   ///
>                         xtitle("{c -(}it: Ordinal Logistic Coefficients{c )-}", size(vsmall))             ///
>                         keep(*.human*) base                                                                                                     ///
>                         name(comparison_coef,replace)  xline(0)                                                                         ///
>                         legend(ring(0) pos(1) rows(3) size(small))                                                      ///
>                         ylab(,labsize(vsmall))                                                                                          ///
>                         note("All  N = `n2', Christian only N = `n2_x', non-Christians N = `n2_notx'"   ///
>                         "Full specification for includes demographic and technical covariates.", size(tiny))
{res}{txt}
{com}. 
. 
. **********************
. * Appendix Table A5
.                                                                 
. esttab comparison_all comparison_xtian comparison_notian                                                ///
>         using "${c -(}MyProject{c )-}AppendixTableA5.tex"                                 ///
>         , replace label nobase  longtable       drop(cut*)                                                              ///
>         title("Humanitarian In Group OutGroup Results" "\label{c -(}tab:humanitarianH2{c )-}")                            ///
>         mtitles("All" "Christians" "Not Christians")
{res}{txt}(output written to {browse  `"~/Dropbox/0001 Academic Projects/Ongoing/0155 Voting Abroad Purpose/Replication/Upload/AppendixTableA5.tex"'})

{com}. 
.         
. /// Produces Figure 2 B)
> 
. est restore comparison_xtian
{txt}(results {stata estimates replay comparison_xtian:comparison_xtian} are active now)

{com}. margins, atmeans dydx(humanitarian_treat_num) post
{res}
{txt}{col 1}Conditional marginal effects{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:443}
{txt}{col 1}Model VCE: {res:OIM}

{txt}{p2colset 1 12 12 2}{...}
{p2col:dy/dx wrt:}{res:2.humanitarian_treat_num3}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 13 13 2}{...}
{p2col:{txt:1._predict:}}{res:Pr(human_main_DV==0), predict(pr outcome(0))}{p_end}
{p2col:{txt:2._predict:}}{res:Pr(human_main_DV==1), predict(pr outcome(1))}{p_end}
{p2col:{txt:3._predict:}}{res:Pr(human_main_DV==2), predict(pr outcome(2))}{p_end}
{p2col:{txt:4._predict:}}{res:Pr(human_main_DV==3), predict(pr outcome(3))}{p_end}
{p2col:{txt:5._predict:}}{res:Pr(human_main_DV==4), predict(pr outcome(4))}{p_end}
{p2col:{txt:6._predict:}}{res:Pr(human_main_DV==5), predict(pr outcome(5))}{p_end}
{p2col:{txt:7._predict:}}{res:Pr(human_main_DV==6), predict(pr outcome(6))}{p_end}
{p2colreset}{...}

{lalign 4:At: }{space 0}{lalign 25:1.humanitarian_treat_num3} = {res:{ralign 8:.5056433}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:2.humanitarian_treat_num3} = {res:{ralign 8:.4943567}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:age} = {res:{ralign 8:44.29571}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:0.female} = {res:{ralign 8:.4920993}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:1.female} = {res:{ralign 8:.5079007}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:0.white} = {res:{ralign 8:.1783296}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:1.white} = {res:{ralign 8:.8216704}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:0.hispanic} = {res:{ralign 8:.9164786}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:1.hispanic} = {res:{ralign 8:.0835214}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:0.college} = {res:{ralign 8:.5711061}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:1.college} = {res:{ralign 8:.4288939}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:0.hhi_num} = {res:{ralign 8:.1625282}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:1.hhi_num} = {res:{ralign 8:.3047404}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:2.hhi_num} = {res:{ralign 8:.3386005}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:3.hhi_num} = {res:{ralign 8:.1715576}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:4.hhi_num} = {res:{ralign 8:.0225734}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:0.pid7} = {res:{ralign 8:.1489842}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:1.pid7} = {res:{ralign 8:.1128668}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:2.pid7} = {res:{ralign 8:.0812641}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:3.pid7} = {res:{ralign 8:.0902935}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:4.pid7} = {res:{ralign 8:.1060948}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:5.pid7} = {res:{ralign 8:.2731377}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:6.pid7} = {res:{ralign 8:.1873589}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:0.employed} = {res:{ralign 8:.1444695}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:1.employed} = {res:{ralign 8:.1399549}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:2.employed} = {res:{ralign 8:.5146727}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:3.employed} = {res:{ralign 8:.2009029}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:1.order} = {res:{ralign 8:.3024831}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:2.order} = {res:{ralign 8:.3566591}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:3.order} = {res:{ralign 8:.3408578}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:0.mobile} = {res:{ralign 8:.6952596}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:1.mobile} = {res:{ralign 8:.3047404}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:0.attention_color} = {res:{ralign 8:.013544}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:1.attention_color} = {res:{ralign 8:.986456}} {txt:(mean)}

{res}{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29} Delta-method
{col 17}{c |}      dy/dx{col 29}   std. err.{col 41}      z{col 49}   P>|z|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}1.humanitaria~3{col 17}{txt}{c |}  (base outcome)
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}2.humanitaria~3 {txt}{c |}
{space 7}_predict {c |}
{space 13}1  {c |}{col 17}{res}{space 2} .0469265{col 29}{space 2} .0191869{col 40}{space 1}    2.45{col 49}{space 3}0.014{col 57}{space 4} .0093209{col 70}{space 3} .0845322
{txt}{space 13}2  {c |}{col 17}{res}{space 2} .0294543{col 29}{space 2} .0122791{col 40}{space 1}    2.40{col 49}{space 3}0.016{col 57}{space 4} .0053877{col 70}{space 3}  .053521
{txt}{space 13}3  {c |}{col 17}{res}{space 2} .0201077{col 29}{space 2} .0086132{col 40}{space 1}    2.33{col 49}{space 3}0.020{col 57}{space 4} .0032262{col 70}{space 3} .0369892
{txt}{space 13}4  {c |}{col 17}{res}{space 2} .0130642{col 29}{space 2} .0063072{col 40}{space 1}    2.07{col 49}{space 3}0.038{col 57}{space 4} .0007023{col 70}{space 3}  .025426
{txt}{space 13}5  {c |}{col 17}{res}{space 2}-.0138414{col 29}{space 2} .0065273{col 40}{space 1}   -2.12{col 49}{space 3}0.034{col 57}{space 4}-.0266347{col 70}{space 3}-.0010481
{txt}{space 13}6  {c |}{col 17}{res}{space 2}-.0430257{col 29}{space 2} .0176169{col 40}{space 1}   -2.44{col 49}{space 3}0.015{col 57}{space 4}-.0775541{col 70}{space 3}-.0084973
{txt}{space 13}7  {c |}{col 17}{res}{space 2}-.0526857{col 29}{space 2} .0215408{col 40}{space 1}   -2.45{col 49}{space 3}0.014{col 57}{space 4}-.0949049{col 70}{space 3}-.0104664
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 81}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. est sto comparison_xtian_dydx, title("Christian")
{txt}
{com}. 
. est restore comparison_notian
{txt}(results {stata estimates replay comparison_notian:comparison_notian} are active now)

{com}. margins, atmeans dydx(humanitarian_treat_num) post
{res}
{txt}{col 1}Conditional marginal effects{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:428}
{txt}{col 1}Model VCE: {res:OIM}

{txt}{p2colset 1 12 12 2}{...}
{p2col:dy/dx wrt:}{res:2.humanitarian_treat_num3}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 13 13 2}{...}
{p2col:{txt:1._predict:}}{res:Pr(human_main_DV==0), predict(pr outcome(0))}{p_end}
{p2col:{txt:2._predict:}}{res:Pr(human_main_DV==1), predict(pr outcome(1))}{p_end}
{p2col:{txt:3._predict:}}{res:Pr(human_main_DV==2), predict(pr outcome(2))}{p_end}
{p2col:{txt:4._predict:}}{res:Pr(human_main_DV==3), predict(pr outcome(3))}{p_end}
{p2col:{txt:5._predict:}}{res:Pr(human_main_DV==4), predict(pr outcome(4))}{p_end}
{p2col:{txt:6._predict:}}{res:Pr(human_main_DV==5), predict(pr outcome(5))}{p_end}
{p2col:{txt:7._predict:}}{res:Pr(human_main_DV==6), predict(pr outcome(6))}{p_end}
{p2colreset}{...}

{lalign 4:At: }{space 0}{lalign 25:1.humanitarian_treat_num3} = {res:{ralign 8:.5163551}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:2.humanitarian_treat_num3} = {res:{ralign 8:.4836449}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:age} = {res:{ralign 8:39.53505}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:0.female} = {res:{ralign 8:.5420561}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:1.female} = {res:{ralign 8:.4579439}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:0.white} = {res:{ralign 8:.2406542}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:1.white} = {res:{ralign 8:.7593458}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:0.hispanic} = {res:{ralign 8:.8808411}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:1.hispanic} = {res:{ralign 8:.1191589}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:0.college} = {res:{ralign 8:.6518692}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:1.college} = {res:{ralign 8:.3481308}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:0.hhi_num} = {res:{ralign 8:.2079439}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:1.hhi_num} = {res:{ralign 8:.2733645}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:2.hhi_num} = {res:{ralign 8:.3434579}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:3.hhi_num} = {res:{ralign 8:.1565421}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:4.hhi_num} = {res:{ralign 8:.0186916}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:0.pid7} = {res:{ralign 8:.2827103}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:1.pid7} = {res:{ralign 8:.1728972}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:2.pid7} = {res:{ralign 8:.1799065}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:3.pid7} = {res:{ralign 8:.1915888}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:4.pid7} = {res:{ralign 8:.0397196}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:5.pid7} = {res:{ralign 8:.0864486}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:6.pid7} = {res:{ralign 8:.046729}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:0.employed} = {res:{ralign 8:.1962617}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:1.employed} = {res:{ralign 8:.1074766}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:2.employed} = {res:{ralign 8:.4485981}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:3.employed} = {res:{ralign 8:.2476636}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:1.order} = {res:{ralign 8:.3060748}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:2.order} = {res:{ralign 8:.3457944}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:3.order} = {res:{ralign 8:.3481308}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:0.mobile} = {res:{ralign 8:.6869159}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:1.mobile} = {res:{ralign 8:.3130841}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:0.attention_color} = {res:{ralign 8:.0046729}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:1.attention_color} = {res:{ralign 8:.9953271}} {txt:(mean)}

{res}{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29} Delta-method
{col 17}{c |}      dy/dx{col 29}   std. err.{col 41}      z{col 49}   P>|z|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}1.humanitaria~3{col 17}{txt}{c |}  (base outcome)
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}2.humanitaria~3 {txt}{c |}
{space 7}_predict {c |}
{space 13}1  {c |}{col 17}{res}{space 2}-.0412005{col 29}{space 2} .0195083{col 40}{space 1}   -2.11{col 49}{space 3}0.035{col 57}{space 4}-.0794361{col 70}{space 3} -.002965
{txt}{space 13}2  {c |}{col 17}{res}{space 2}-.0250329{col 29}{space 2} .0121179{col 40}{space 1}   -2.07{col 49}{space 3}0.039{col 57}{space 4}-.0487836{col 70}{space 3}-.0012823
{txt}{space 13}3  {c |}{col 17}{res}{space 2}-.0188908{col 29}{space 2} .0092563{col 40}{space 1}   -2.04{col 49}{space 3}0.041{col 57}{space 4}-.0370329{col 70}{space 3}-.0007488
{txt}{space 13}4  {c |}{col 17}{res}{space 2}-.0088976{col 29}{space 2} .0049176{col 40}{space 1}   -1.81{col 49}{space 3}0.070{col 57}{space 4} -.018536{col 70}{space 3} .0007408
{txt}{space 13}5  {c |}{col 17}{res}{space 2} .0137789{col 29}{space 2} .0072248{col 40}{space 1}    1.91{col 49}{space 3}0.057{col 57}{space 4}-.0003816{col 70}{space 3} .0279393
{txt}{space 13}6  {c |}{col 17}{res}{space 2} .0407966{col 29}{space 2} .0194308{col 40}{space 1}    2.10{col 49}{space 3}0.036{col 57}{space 4} .0027129{col 70}{space 3} .0788802
{txt}{space 13}7  {c |}{col 17}{res}{space 2} .0394464{col 29}{space 2} .0188097{col 40}{space 1}    2.10{col 49}{space 3}0.036{col 57}{space 4}   .00258{col 70}{space 3} .0763128
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 81}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. est sto comparison_notian_dydx,title("Muslim")
{txt}
{com}. 
. est restore comparison_all 
{txt}(results {stata estimates replay comparison_all:comparison_all} are active now)

{com}. margins, atmeans  dydx(humanitarian_treat_num) post
{res}
{txt}{col 1}Conditional marginal effects{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:934}
{txt}{col 1}Model VCE: {res:OIM}

{txt}{p2colset 1 12 12 2}{...}
{p2col:dy/dx wrt:}{res:2.humanitarian_treat_num3}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 13 13 2}{...}
{p2col:{txt:1._predict:}}{res:Pr(human_main_DV==0), predict(pr outcome(0))}{p_end}
{p2col:{txt:2._predict:}}{res:Pr(human_main_DV==1), predict(pr outcome(1))}{p_end}
{p2col:{txt:3._predict:}}{res:Pr(human_main_DV==2), predict(pr outcome(2))}{p_end}
{p2col:{txt:4._predict:}}{res:Pr(human_main_DV==3), predict(pr outcome(3))}{p_end}
{p2col:{txt:5._predict:}}{res:Pr(human_main_DV==4), predict(pr outcome(4))}{p_end}
{p2col:{txt:6._predict:}}{res:Pr(human_main_DV==5), predict(pr outcome(5))}{p_end}
{p2col:{txt:7._predict:}}{res:Pr(human_main_DV==6), predict(pr outcome(6))}{p_end}
{p2colreset}{...}

{lalign 4:At: }{space 0}{lalign 25:1.humanitarian_treat_num3} = {res:{ralign 8:.5139186}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:2.humanitarian_treat_num3} = {res:{ralign 8:.4860814}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:age} = {res:{ralign 8:42.32548}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:0.female} = {res:{ralign 8:.5117773}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:1.female} = {res:{ralign 8:.4882227}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:0.white} = {res:{ralign 8:.2098501}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:1.white} = {res:{ralign 8:.7901499}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:0.hispanic} = {res:{ralign 8:.8993576}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:1.hispanic} = {res:{ralign 8:.1006424}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:0.college} = {res:{ralign 8:.6188437}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:1.college} = {res:{ralign 8:.3811563}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:0.hhi_num} = {res:{ralign 8:.1895075}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:1.hhi_num} = {res:{ralign 8:.3051392}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:2.hhi_num} = {res:{ralign 8:.3297645}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:3.hhi_num} = {res:{ralign 8:.1563169}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:4.hhi_num} = {res:{ralign 8:.0192719}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:0.pid7} = {res:{ralign 8:.2162741}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:1.pid7} = {res:{ralign 8:.1391863}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:2.pid7} = {res:{ralign 8:.133833}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:3.pid7} = {res:{ralign 8:.1477516}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:4.pid7} = {res:{ralign 8:.0728051}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:5.pid7} = {res:{ralign 8:.1745182}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:6.pid7} = {res:{ralign 8:.1156317}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:0.employed} = {res:{ralign 8:.1766595}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:1.employed} = {res:{ralign 8:.127409}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:2.employed} = {res:{ralign 8:.4796574}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:3.employed} = {res:{ralign 8:.2162741}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:1.order} = {res:{ralign 8:.3051392}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:2.order} = {res:{ralign 8:.3522484}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:3.order} = {res:{ralign 8:.3426124}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:0.mobile} = {res:{ralign 8:.6852248}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:1.mobile} = {res:{ralign 8:.3147752}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:0.attention_color} = {res:{ralign 8:.0085653}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 25:1.attention_color} = {res:{ralign 8:.9914347}} {txt:(mean)}

{res}{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29} Delta-method
{col 17}{c |}      dy/dx{col 29}   std. err.{col 41}      z{col 49}   P>|z|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}1.humanitaria~3{col 17}{txt}{c |}  (base outcome)
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}2.humanitaria~3 {txt}{c |}
{space 7}_predict {c |}
{space 13}1  {c |}{col 17}{res}{space 2} .0160702{col 29}{space 2}  .013242{col 40}{space 1}    1.21{col 49}{space 3}0.225{col 57}{space 4}-.0098836{col 70}{space 3}  .042024
{txt}{space 13}2  {c |}{col 17}{res}{space 2} .0094958{col 29}{space 2} .0078428{col 40}{space 1}    1.21{col 49}{space 3}0.226{col 57}{space 4}-.0058758{col 70}{space 3} .0248673
{txt}{space 13}3  {c |}{col 17}{res}{space 2}  .006258{col 29}{space 2} .0051866{col 40}{space 1}    1.21{col 49}{space 3}0.228{col 57}{space 4}-.0039076{col 70}{space 3} .0164236
{txt}{space 13}4  {c |}{col 17}{res}{space 2} .0035529{col 29}{space 2} .0029976{col 40}{space 1}    1.19{col 49}{space 3}0.236{col 57}{space 4}-.0023223{col 70}{space 3} .0094282
{txt}{space 13}5  {c |}{col 17}{res}{space 2}-.0045099{col 29}{space 2} .0037972{col 40}{space 1}   -1.19{col 49}{space 3}0.235{col 57}{space 4}-.0119522{col 70}{space 3} .0029324
{txt}{space 13}6  {c |}{col 17}{res}{space 2}-.0141317{col 29}{space 2} .0116352{col 40}{space 1}   -1.21{col 49}{space 3}0.225{col 57}{space 4}-.0369362{col 70}{space 3} .0086728
{txt}{space 13}7  {c |}{col 17}{res}{space 2}-.0167353{col 29}{space 2} .0137862{col 40}{space 1}   -1.21{col 49}{space 3}0.225{col 57}{space 4}-.0437557{col 70}{space 3} .0102851
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 81}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. est sto comparison_all_dydx,title("All")
{txt}
{com}. 
. mylabels -10(5)10, myscale(@/100) local(myla)
{res}{p}                -.1 "-10"                -.05 "-5"                   0 "0"                 .05 "5"                  .1 "10" 
{txt}
{smcl}
{com}. 
. coefplot (comparison_all_dydx, label("All Rs")) ///
>                  (comparison_xtian_dydx, label("Christian Rs")) ///
>                  (comparison_notian_dydx, label("Non-Christian Rs")) ///
>                  , recast(bar) barw(0.15) vertical  ///
>                         ciopts(recast(rcap) color(gs8)) citop ///
>                         xlab(1 `""Strongly" "disapprove""' 2 "Disapprove" 3 `""Somewhat" "disapprove""'         ///
>                                 4 "Neither" 5 `""Somewhat" "approve""' 6 "Approve" 7 `""Strongly" "approve""',  ///
>                                 labsize(vsmall))                                                                                                ///
>                 title("{c -(}bf: B) Predicted Changes in Predicted Support{c )-}", size(medsmall)) ///
>                 subtitle("{c -(}it:Difference in Support Moving from Christian to Muslim ID Treatments{c )-}", size(vsmall)) ///
>                 name(gph_dydx_xtian_ologit, replace) legend(rows(1) ring(2) pos(12)) ///
>                 note("All variables at sample means.", size(tiny)) ///
>                                 yline(0) ylab(`myla',labsize(vsmall))   
{res}{txt}
{com}. 
. **********************
. * Main Paper Figure 2
.                         
. gr combine comparison_coef gph_dydx_xtian_ologit, rows(1)                       
{res}{txt}
{com}. 
. graph display, ysize(5) xsize(8)
{res}{txt}
{com}. 
. gr export "${c -(}MyProject{c )-}00 Main Figure 2.pdf", replace
{txt}{p 0 4 2}
file {bf}
~/Dropbox/0001 Academic Projects/Ongoing/0155 Voting Abroad Purpose/Replication/Upload/00 Main Figure 2.pdf{rm}
saved as
PDF
format
{p_end}

{com}. 
. /*
>         Additional analysis regarding Christian and Muslim treatments for the appendix.
> */
. 
. /// Appendix Figures A3 and A4
> 
. lab def religion_attend_reglab 0 "Does not attend regularly" 1 "Attends regularly"
{txt}
{com}. 
. lab val religion_attend_reg religion_attend_reglab
{txt}
{com}. 
. * * * * * * * * * * * *
. * Religious Attendance          
. 
. * With Neithers 
. 
. quietly ologit human_main_DV b2.humanitarian_treat_num3##i.religion_attend_reg          ///
>                         if humanitarian_treat_num3 >=1 & christian == 1
{txt}
{com}. est sto h2a_attend_ologit_base
{txt}
{com}. local sto h2a_attend_ologit_base_N = e(N)
{txt}
{com}. 
. quietly ologit human_main_DV b2.humanitarian_treat_num3##i.religion_attend_reg          ///
>                 `technical' `demographics'                                                                                              ///
>                         if humanitarian_treat_num3 >=1 & christian == 1
{txt}
{com}. est sto h2a_attend_ologit_cov
{txt}
{com}. local h2a_attend_ologit_cov_N = e(N)
{txt}
{com}. 
. * Without Neithers 
. 
. quietly ologit human_main_DV2 b2.humanitarian_treat_num3##i.religion_attend_reg         ///
>                         if humanitarian_treat_num3 >=1 & christian == 1
{txt}
{com}. est sto h2a_attend_ologit_base_alt
{txt}
{com}. local h2a_attend_ologit_base_alt_N = e(N)
{txt}
{com}. 
. quietly ologit human_main_DV2 b2.humanitarian_treat_num3##i.religion_attend_reg         ///
>                 `technical' `demographics'                                                                                              ///
>                         if humanitarian_treat_num3 >=1 & christian == 1
{txt}
{com}. est sto h2a_attend_ologit_cov_alt
{txt}
{com}. local h2a_attend_ologit_cov_alt_N = e(N)
{txt}
{com}. 
. * Graphics (Attendance)
. 
. local model1title "Base"
{txt}
{com}. local model2title "Base, Alt DV"
{txt}
{com}. local model3title "Adj"
{txt}
{com}. local model4title "Adj, Alt DV"
{txt}
{com}.                 
. est restore h2a_attend_ologit_base
{txt}(results {stata estimates replay h2a_attend_ologit_base:h2a_attend_ologit_base} are active now)

{com}. local model1n = e(N)
{txt}
{com}. 
. est restore h2a_attend_ologit_base_alt
{txt}(results {stata estimates replay h2a_attend_ologit_base_alt:h2a_attend_ologit_base_alt} are active now)

{com}. local model2n = e(N)
{txt}
{com}. 
. est restore h2a_attend_ologit_cov
{txt}(results {stata estimates replay h2a_attend_ologit_cov:h2a_attend_ologit_cov} are active now)

{com}. local model3n = e(N)
{txt}
{com}. 
. est restore h2a_attend_ologit_cov_alt
{txt}(results {stata estimates replay h2a_attend_ologit_cov_alt:h2a_attend_ologit_cov_alt} are active now)

{com}. local model4n = e(N)
{txt}
{com}. 
. local gtitle g_h2a_attend_ologit
{txt}
{com}. 
. **********************
. * Appendix Figure A3
.                         
. coefplot        (h2a_attend_ologit_base, label("Base"))                                                         ///
>                         (h2a_attend_ologit_base_alt, label("Base, Alt DV"))                             ///
>                         (h2a_attend_ologit_cov, label(Adj))                                                             ///
>                         (h2a_attend_ologit_cov_alt, label(Adj, Alt DV))                                         ///
>                         , title("{c -(}bf: Religious Attendance and Intervention Support Among Christians{c )-}")         ///
>                         xtitle("{c -(}it: Ordinal Logistic Coefficients{c )-}")                                           ///
>                          base                                                                                                                           ///
>                          keep(*.humanitarian_treat_num3* *.religion_attend_reg)                         ///
>                         name(`gtitle',replace)  xline(0)                                                                        ///
>                         legend(ring(0) pos(10) rows(4) size(small))                                             ///
>                         ylab(,labsize(vsmall))                                                                                          ///
>                         note("`model1title' N = `model1n', `model2title' N = `model2n', `model3title' N = `model3n', `model4title' N = `model4n'"                                                                                               ///
>                         "Full specification for Adjusted includes demographic and technical covariates. Alt DVs omit 'neither' categories.", size(vsmall))
{res}{txt}
{com}.                         
. gr export "${c -(}MyProject{c )-}01 Figure A3.pdf", replace
{txt}{p 0 4 2}
file {bf}
~/Dropbox/0001 Academic Projects/Ongoing/0155 Voting Abroad Purpose/Replication/Upload/01 Figure A3.pdf{rm}
saved as
PDF
format
{p_end}

{com}. 
. 
. * * * * * * * * * * * *
. * Religion Importance
. 
. * With Neithers
. 
. quietly ologit human_main_DV b2.humanitarian_treat_num3##c.religionimpt_num                                     ///
>                         if humanitarian_treat_num3 >=1 & christian == 1
{txt}
{com}. est sto h2a_import_ologit_base
{txt}
{com}. local h2a_import_ologit_base_N = e(N)
{txt}
{com}. 
. quietly ologit human_main_DV b2.humanitarian_treat_num3##c.religionimpt_num                                     ///
>                         `technical'                                                                                                                             ///
>                         if humanitarian_treat_num3 >=1 & christian == 1
{txt}
{com}. est sto h2a_import_ologit_tech
{txt}
{com}. local h2a_import_ologit_tech_N = e(N)
{txt}
{com}.                         
. quietly ologit human_main_DV b2.humanitarian_treat_num3##c.religionimpt_num                                     ///
>                         `technical' `demographics'                                                                                              ///
>                         if humanitarian_treat_num3 >=1 & christian == 1
{txt}
{com}. est sto h2a_import_ologit_cov
{txt}
{com}. local h2a_import_ologit_cov_N = e(N)
{txt}
{com}. 
. * Without Neithers
. 
. quietly ologit human_main_DV2 b2.humanitarian_treat_num3##c.religionimpt_num                            ///
>                         if humanitarian_treat_num3 >=1 & christian == 1
{txt}
{com}. est sto h2a_import_ologit_base_alt
{txt}
{com}. local h2a_import_ologit_base_alt_N = e(N)
{txt}
{com}. 
. quietly ologit human_main_DV2 b2.humanitarian_treat_num3##c.religionimpt_num                            ///
>                         `technical'                                                                                                                             ///
>                         if humanitarian_treat_num3 >=1 & christian == 1
{txt}
{com}. est sto h2a_import_ologit_tech_alt
{txt}
{com}. local h2a_import_ologit_tech_alt_N = e(N)
{txt}
{com}.                         
. quietly ologit human_main_DV2 b2.humanitarian_treat_num3##c.religionimpt_num                            ///
>                         `technical' `demographics'                                                                                              ///
>                         if humanitarian_treat_num3 >=1 & christian == 1
{txt}
{com}. est sto h2a_import_ologit_cov_alt
{txt}
{com}. local sto h2a_import_ologit_cov_alt_N = e(N)
{txt}
{com}. 
. * Graphics (Importance)
. 
. local model1 h2a_import_ologit_base
{txt}
{com}. local model2 h2a_import_ologit_base_alt
{txt}
{com}. local model3 h2a_import_ologit_cov
{txt}
{com}. local model4 h2a_import_ologit_cov_alt
{txt}
{com}. 
. local model1title "Base"
{txt}
{com}. local model2title "Base, Alt DV"
{txt}
{com}. local model3title "Adj"
{txt}
{com}. local model4title "Adj, Alt DV"
{txt}
{com}.                 
. est restore `model1'
{txt}(results {stata estimates replay h2a_import_ologit_base:h2a_import_ologit_base} are active now)

{com}. local model1n = e(N)
{txt}
{com}. 
. est restore `model2'
{txt}(results {stata estimates replay h2a_import_ologit_base_alt:h2a_import_ologit_base_alt} are active now)

{com}. local model2n = e(N)
{txt}
{com}. 
. est restore `model3'
{txt}(results {stata estimates replay h2a_import_ologit_cov:h2a_import_ologit_cov} are active now)

{com}. local model3n = e(N)
{txt}
{com}. 
. est restore `model4'
{txt}(results {stata estimates replay h2a_import_ologit_cov_alt:h2a_import_ologit_cov_alt} are active now)

{com}. local model4n = e(N)
{txt}
{com}. 
. local gtitle g_h2a_import_ologit
{txt}
{com}. 
. **********************
. * Appendix Figure A4
. 
. coefplot        (`model1', label(`model1title'))                                                                        ///
>                         (`model2', label(`model2title'))                                                                        ///
>                         (`model3', label(`model3title'))                                                                        ///
>                         (`model4', label(`model4title'))                                                                        ///
>                         , title("{c -(}bf: Religious Importance and Intervention Support Among Christians{c )-}")         ///
>                         xtitle("{c -(}it: Ordinal Logistic Coefficients{c )-}")                                           ///
>                          keep(*.humanitarian_treat_num3* religionimpt_num)                                      ///
>                         base                                                                                                                            ///
>                         name(`gtitle',replace)  xline(0)                                                                        ///
>                         legend(ring(0) pos(4) rows(4) size(small))                                              ///
>                         ylab(,labsize(vsmall))                                                                                          ///
>                         note("`model1title' N = `model1n', `model2title' N = `model2n', `model3title' N = `model3n', `model4title' N = `model4n'"                                                                                               ///
>                         "Full specification for Adjusted includes demographic and technical covariates. Alt DVs omit 'neither' categories.", size(vsmall))
{res}{txt}
{com}.         
. gr export "${c -(}MyProject{c )-}01 Figure A4.pdf", replace
{txt}{p 0 4 2}
file {bf}
~/Dropbox/0001 Academic Projects/Ongoing/0155 Voting Abroad Purpose/Replication/Upload/01 Figure A4.pdf{rm}
saved as
PDF
format
{p_end}

{com}. 
.                         
. ********************************************************************************
. * Substitutability and Strategy Preferences (H3 and H3.1)
. ********************************************************************************
. 
. * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * 
. * Reshaping and re-cleaning data for strategies/substitutability
. * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * 
. 
. keep order mobile attention_color age female white hispanic college hhi_num     ///
>                         pid7 employed human_contribute* human_negotiate* human_coup*            ///
>                         human_concession* human_threaten* humanitarian_treat_* id
{txt}
{com}.                         
. rename human_*_DV human_DV_ordinal_*
{res}{txt}
{com}. rename human_*_DV2 human_DV2_*
{res}{txt}
{com}. rename human_*_DV_dichot1 human_DV_dichot1_*
{res}{txt}
{com}. rename human_*_DV_dichot2 human_DV_dichot2_*
{res}{txt}
{com}. rename human_*_DV_trichot human_DV_trichot_*
{res}{txt}
{com}. 
. 
. reshape long human_DV_ordinal_ human_DV2_ human_DV_dichot1_ human_DV_dichot2_   ///
>                 human_DV_trichot_                                                                                                               ///
>                 , i(id) j(strategy,string)
{txt}(j = concession contribute coup negotiate threaten)

Data{col 36}Wide{col 43}->{col 48}Long
{hline 77}
Number of observations     {res}       1,515   {txt}->   {res}7,575       
{txt}Number of variables        {res}          44   {txt}->   {res}25          
{txt}j variable (5 values)                     ->   {res}strategy
{txt}xij variables:
{res}human_DV_ordinal_concession human_DV_ordinal_contribute ... human_DV_ordinal_threaten{txt}->{res}human_DV_ordinal_
human_DV2_concession human_DV2_contribute ... human_DV2_threaten{txt}->{res}human_DV2_
human_DV_dichot1_concession human_DV_dichot1_contribute ... human_DV_dichot1_threaten{txt}->{res}human_DV_dichot1_
human_DV_dichot2_concession human_DV_dichot2_contribute ... human_DV_dichot2_threaten{txt}->{res}human_DV_dichot2_
human_DV_trichot_concession human_DV_trichot_contribute ... human_DV_trichot_threaten{txt}->{res}human_DV_trichot_
{txt}{hline 77}

{com}. 
. rename *_ *
{res}{txt}
{com}. 
. 
. encode strategy, gen(strategy_num)
{txt}
{com}. 
. 
. * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * 
. * Run models for each strategy
. * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * 
. 
. 
. forvalues i = 1/5 {c -(}
{txt}  2{com}.         local modtitle: label (strategy_num) `i'
{txt}  3{com}.         * Covariates
.         local modlab "c"
{txt}  4{com}.         local dvlab "ordinal"
{txt}  5{com}.         quietly ologit human_DV_ordinal i.humanitarian_treat_num `technical' `demographics'     ///
>                 if strategy_num == `i'
{txt}  6{com}.         eststo `dvlab'_`modtitle'_`modlab', title("Ordinal")    
{txt}  7{com}.                 
. {c )-}
{txt}
{com}. 
. 
. * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * 
. * Graph the five models (coefplot)
. * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * 
. 
.                 local model1 ordinal_concession_c
{txt}
{com}.                 local model2 ordinal_negotiate_c
{txt}
{com}.                 local model3 ordinal_contribute_c
{txt}
{com}.                 local model4 ordinal_coup_c
{txt}
{com}.                 local model5 ordinal_threaten_c
{txt}
{com}.                 
.                 local model1title "Concession After"
{txt}
{com}.                 local model2title "Concession Before"
{txt}
{com}.                 local model3title "Contribute"
{txt}
{com}.                 local model4title "Encourage Coup"
{txt}
{com}.                 local model5title "Sanctions"
{txt}
{com}.                 
.                 
.                 
.                 est restore `model1'
{txt}(results {stata estimates replay ordinal_concession_c:ordinal_concession_c} are active now)

{com}.                 local model1n = e(N)
{txt}
{com}. 
.                 est restore `model2'
{txt}(results {stata estimates replay ordinal_negotiate_c:ordinal_negotiate_c} are active now)

{com}.                 local model2n = e(N)
{txt}
{com}.                 
.                 est restore `model3'
{txt}(results {stata estimates replay ordinal_contribute_c:ordinal_contribute_c} are active now)

{com}.                 local model3n = e(N)
{txt}
{com}. 
.                 est restore `model4'
{txt}(results {stata estimates replay ordinal_coup_c:ordinal_coup_c} are active now)

{com}.                 local model4n = e(N)
{txt}
{com}. 
.                 est restore `model5'
{txt}(results {stata estimates replay ordinal_threaten_c:ordinal_threaten_c} are active now)

{com}.                 local model5n = e(N)
{txt}
{com}.                 
.         coefplot                                                                                                                                        ///
>                                 (`model1', label(`model1title' (`model1n')))                                    ///
>                                 (`model2', label(`model2title' (`model2n')))                                    ///
>                                 (`model3', label(`model3title' (`model3n')))                                    ///
>                                 (`model4', label(`model4title' (`model4n')))                                    ///
>                                 (`model5', label(`model5title' (`model5n')))                                    ///
>                                 , title("{c -(}bf: B) Effects of Treatment on Option Support{c )-}", size(medsmall) span)         ///
>                                 xtitle("{c -(}it:Ologit Coefficients{c )-}",size(small))                                                          ///
>                                 keep(*.human*) base     xline(0)                                                                        ///
>                                 legend(ring(0) pos(2) rows(5) size(small))                                              ///
>                                 note("Ns in parentheses. Full specification includes demographic and technical covariates.", size(tiny))                ///
>                         name(ologitcoef_small,replace)
{res}{txt}
{com}. 
. * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * 
. * Substantive Plots (DYDX)
. * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * 
. 
. est restore ordinal_threaten_c
{txt}(results {stata estimates replay ordinal_threaten_c:ordinal_threaten_c} are active now)

{com}.  margins , atmeans dydx(humanitarian_treat_num) post
{res}
{txt}{col 1}Conditional marginal effects{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:1,364}
{txt}{col 1}Model VCE: {res:OIM}

{txt}{p2colset 1 12 12 2}{...}
{p2col:dy/dx wrt:}{res:1.humanitarian_treat_num}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 13 13 2}{...}
{p2col:{txt:1._predict:}}{res:Pr(human_DV_ordinal==0), predict(pr outcome(0))}{p_end}
{p2col:{txt:2._predict:}}{res:Pr(human_DV_ordinal==1), predict(pr outcome(1))}{p_end}
{p2col:{txt:3._predict:}}{res:Pr(human_DV_ordinal==2), predict(pr outcome(2))}{p_end}
{p2col:{txt:4._predict:}}{res:Pr(human_DV_ordinal==3), predict(pr outcome(3))}{p_end}
{p2col:{txt:5._predict:}}{res:Pr(human_DV_ordinal==4), predict(pr outcome(4))}{p_end}
{p2col:{txt:6._predict:}}{res:Pr(human_DV_ordinal==5), predict(pr outcome(5))}{p_end}
{p2col:{txt:7._predict:}}{res:Pr(human_DV_ordinal==6), predict(pr outcome(6))}{p_end}
{p2colreset}{...}

{lalign 4:At: }{space 0}{lalign 24:0.humanitarian_treat_num} = {res:{ralign 8:.3152493}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:1.humanitarian_treat_num} = {res:{ralign 8:.6847507}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:1.order} = {res:{ralign 8:.3167155}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:2.order} = {res:{ralign 8:.3504399}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:3.order} = {res:{ralign 8:.3328446}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:0.mobile} = {res:{ralign 8:.691349}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:1.mobile} = {res:{ralign 8:.308651}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:0.attention_color} = {res:{ralign 8:.0073314}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:1.attention_color} = {res:{ralign 8:.9926686}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:age} = {res:{ralign 8:42.05718}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:0.female} = {res:{ralign 8:.5021994}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:1.female} = {res:{ralign 8:.4978006}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:0.white} = {res:{ralign 8:.2162757}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:1.white} = {res:{ralign 8:.7837243}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:0.hispanic} = {res:{ralign 8:.8958944}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:1.hispanic} = {res:{ralign 8:.1041056}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:0.college} = {res:{ralign 8:.5960411}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:1.college} = {res:{ralign 8:.4039589}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:0.hhi_num} = {res:{ralign 8:.1788856}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:1.hhi_num} = {res:{ralign 8:.2961877}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:2.hhi_num} = {res:{ralign 8:.3482405}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:3.hhi_num} = {res:{ralign 8:.1539589}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:4.hhi_num} = {res:{ralign 8:.0227273}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:0.pid7} = {res:{ralign 8:.2162757}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:1.pid7} = {res:{ralign 8:.1480938}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:2.pid7} = {res:{ralign 8:.1231672}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:3.pid7} = {res:{ralign 8:.1517595}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:4.pid7} = {res:{ralign 8:.0755132}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:5.pid7} = {res:{ralign 8:.164956}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:6.pid7} = {res:{ralign 8:.1202346}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:0.employed} = {res:{ralign 8:.1678886}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:1.employed} = {res:{ralign 8:.1246334}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:2.employed} = {res:{ralign 8:.4941349}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:3.employed} = {res:{ralign 8:.2133431}} {txt:(mean)}

{res}{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29} Delta-method
{col 17}{c |}      dy/dx{col 29}   std. err.{col 41}      z{col 49}   P>|z|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0.humanitaria~m{col 17}{txt}{c |}  (base outcome)
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.humanitaria~m {txt}{c |}
{space 7}_predict {c |}
{space 13}1  {c |}{col 17}{res}{space 2} -.102989{col 29}{space 2} .0149043{col 40}{space 1}   -6.91{col 49}{space 3}0.000{col 57}{space 4}-.1322008{col 70}{space 3}-.0737771
{txt}{space 13}2  {c |}{col 17}{res}{space 2}-.0511342{col 29}{space 2} .0075075{col 40}{space 1}   -6.81{col 49}{space 3}0.000{col 57}{space 4}-.0658486{col 70}{space 3}-.0364198
{txt}{space 13}3  {c |}{col 17}{res}{space 2} -.031215{col 29}{space 2} .0046285{col 40}{space 1}   -6.74{col 49}{space 3}0.000{col 57}{space 4}-.0402866{col 70}{space 3}-.0221434
{txt}{space 13}4  {c |}{col 17}{res}{space 2}-.0187794{col 29}{space 2} .0035921{col 40}{space 1}   -5.23{col 49}{space 3}0.000{col 57}{space 4}-.0258198{col 70}{space 3} -.011739
{txt}{space 13}5  {c |}{col 17}{res}{space 2} .0291293{col 29}{space 2} .0059777{col 40}{space 1}    4.87{col 49}{space 3}0.000{col 57}{space 4} .0174133{col 70}{space 3} .0408453
{txt}{space 13}6  {c |}{col 17}{res}{space 2} .0810432{col 29}{space 2}  .010957{col 40}{space 1}    7.40{col 49}{space 3}0.000{col 57}{space 4} .0595679{col 70}{space 3} .1025186
{txt}{space 13}7  {c |}{col 17}{res}{space 2}  .093945{col 29}{space 2} .0114512{col 40}{space 1}    8.20{col 49}{space 3}0.000{col 57}{space 4} .0715011{col 70}{space 3} .1163889
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 81}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}.  eststo ordinal_threaten_c_dydx, title("Threaten")
{txt}
{com}.         
. est restore ordinal_contribute_c
{txt}(results {stata estimates replay ordinal_contribute_c:ordinal_contribute_c} are active now)

{com}.  margins , atmeans dydx(humanitarian_treat_num) post
{res}
{txt}{col 1}Conditional marginal effects{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:1,364}
{txt}{col 1}Model VCE: {res:OIM}

{txt}{p2colset 1 12 12 2}{...}
{p2col:dy/dx wrt:}{res:1.humanitarian_treat_num}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 13 13 2}{...}
{p2col:{txt:1._predict:}}{res:Pr(human_DV_ordinal==0), predict(pr outcome(0))}{p_end}
{p2col:{txt:2._predict:}}{res:Pr(human_DV_ordinal==1), predict(pr outcome(1))}{p_end}
{p2col:{txt:3._predict:}}{res:Pr(human_DV_ordinal==2), predict(pr outcome(2))}{p_end}
{p2col:{txt:4._predict:}}{res:Pr(human_DV_ordinal==3), predict(pr outcome(3))}{p_end}
{p2col:{txt:5._predict:}}{res:Pr(human_DV_ordinal==4), predict(pr outcome(4))}{p_end}
{p2col:{txt:6._predict:}}{res:Pr(human_DV_ordinal==5), predict(pr outcome(5))}{p_end}
{p2col:{txt:7._predict:}}{res:Pr(human_DV_ordinal==6), predict(pr outcome(6))}{p_end}
{p2colreset}{...}

{lalign 4:At: }{space 0}{lalign 24:0.humanitarian_treat_num} = {res:{ralign 8:.3152493}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:1.humanitarian_treat_num} = {res:{ralign 8:.6847507}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:1.order} = {res:{ralign 8:.3167155}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:2.order} = {res:{ralign 8:.3504399}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:3.order} = {res:{ralign 8:.3328446}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:0.mobile} = {res:{ralign 8:.691349}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:1.mobile} = {res:{ralign 8:.308651}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:0.attention_color} = {res:{ralign 8:.0073314}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:1.attention_color} = {res:{ralign 8:.9926686}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:age} = {res:{ralign 8:42.05718}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:0.female} = {res:{ralign 8:.5021994}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:1.female} = {res:{ralign 8:.4978006}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:0.white} = {res:{ralign 8:.2162757}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:1.white} = {res:{ralign 8:.7837243}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:0.hispanic} = {res:{ralign 8:.8958944}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:1.hispanic} = {res:{ralign 8:.1041056}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:0.college} = {res:{ralign 8:.5960411}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:1.college} = {res:{ralign 8:.4039589}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:0.hhi_num} = {res:{ralign 8:.1788856}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:1.hhi_num} = {res:{ralign 8:.2961877}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:2.hhi_num} = {res:{ralign 8:.3482405}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:3.hhi_num} = {res:{ralign 8:.1539589}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:4.hhi_num} = {res:{ralign 8:.0227273}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:0.pid7} = {res:{ralign 8:.2162757}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:1.pid7} = {res:{ralign 8:.1480938}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:2.pid7} = {res:{ralign 8:.1231672}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:3.pid7} = {res:{ralign 8:.1517595}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:4.pid7} = {res:{ralign 8:.0755132}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:5.pid7} = {res:{ralign 8:.164956}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:6.pid7} = {res:{ralign 8:.1202346}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:0.employed} = {res:{ralign 8:.1678886}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:1.employed} = {res:{ralign 8:.1246334}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:2.employed} = {res:{ralign 8:.4941349}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:3.employed} = {res:{ralign 8:.2133431}} {txt:(mean)}

{res}{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29} Delta-method
{col 17}{c |}      dy/dx{col 29}   std. err.{col 41}      z{col 49}   P>|z|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0.humanitaria~m{col 17}{txt}{c |}  (base outcome)
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.humanitaria~m {txt}{c |}
{space 7}_predict {c |}
{space 13}1  {c |}{col 17}{res}{space 2}-.0484415{col 29}{space 2} .0131612{col 40}{space 1}   -3.68{col 49}{space 3}0.000{col 57}{space 4}-.0742371{col 70}{space 3} -.022646
{txt}{space 13}2  {c |}{col 17}{res}{space 2}-.0283603{col 29}{space 2} .0074977{col 40}{space 1}   -3.78{col 49}{space 3}0.000{col 57}{space 4}-.0430554{col 70}{space 3}-.0136651
{txt}{space 13}3  {c |}{col 17}{res}{space 2}-.0154304{col 29}{space 2} .0039867{col 40}{space 1}   -3.87{col 49}{space 3}0.000{col 57}{space 4}-.0232442{col 70}{space 3}-.0076166
{txt}{space 13}4  {c |}{col 17}{res}{space 2}-.0091472{col 29}{space 2} .0024383{col 40}{space 1}   -3.75{col 49}{space 3}0.000{col 57}{space 4}-.0139262{col 70}{space 3}-.0043682
{txt}{space 13}5  {c |}{col 17}{res}{space 2} .0124811{col 29}{space 2} .0040458{col 40}{space 1}    3.08{col 49}{space 3}0.002{col 57}{space 4} .0045515{col 70}{space 3} .0204106
{txt}{space 13}6  {c |}{col 17}{res}{space 2}  .041687{col 29}{space 2}  .010831{col 40}{space 1}    3.85{col 49}{space 3}0.000{col 57}{space 4} .0204586{col 70}{space 3} .0629155
{txt}{space 13}7  {c |}{col 17}{res}{space 2} .0472113{col 29}{space 2} .0115602{col 40}{space 1}    4.08{col 49}{space 3}0.000{col 57}{space 4} .0245537{col 70}{space 3} .0698689
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 81}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}.  eststo ordinal_contribute_c_dydx, title("Contribute")
{txt}
{com}.         
. est restore ordinal_coup_c
{txt}(results {stata estimates replay ordinal_coup_c:ordinal_coup_c} are active now)

{com}.  margins , atmeans dydx(humanitarian_treat_num) post
{res}
{txt}{col 1}Conditional marginal effects{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:1,364}
{txt}{col 1}Model VCE: {res:OIM}

{txt}{p2colset 1 12 12 2}{...}
{p2col:dy/dx wrt:}{res:1.humanitarian_treat_num}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 13 13 2}{...}
{p2col:{txt:1._predict:}}{res:Pr(human_DV_ordinal==0), predict(pr outcome(0))}{p_end}
{p2col:{txt:2._predict:}}{res:Pr(human_DV_ordinal==1), predict(pr outcome(1))}{p_end}
{p2col:{txt:3._predict:}}{res:Pr(human_DV_ordinal==2), predict(pr outcome(2))}{p_end}
{p2col:{txt:4._predict:}}{res:Pr(human_DV_ordinal==3), predict(pr outcome(3))}{p_end}
{p2col:{txt:5._predict:}}{res:Pr(human_DV_ordinal==4), predict(pr outcome(4))}{p_end}
{p2col:{txt:6._predict:}}{res:Pr(human_DV_ordinal==5), predict(pr outcome(5))}{p_end}
{p2col:{txt:7._predict:}}{res:Pr(human_DV_ordinal==6), predict(pr outcome(6))}{p_end}
{p2colreset}{...}

{lalign 4:At: }{space 0}{lalign 24:0.humanitarian_treat_num} = {res:{ralign 8:.3152493}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:1.humanitarian_treat_num} = {res:{ralign 8:.6847507}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:1.order} = {res:{ralign 8:.3167155}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:2.order} = {res:{ralign 8:.3504399}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:3.order} = {res:{ralign 8:.3328446}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:0.mobile} = {res:{ralign 8:.691349}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:1.mobile} = {res:{ralign 8:.308651}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:0.attention_color} = {res:{ralign 8:.0073314}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:1.attention_color} = {res:{ralign 8:.9926686}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:age} = {res:{ralign 8:42.05718}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:0.female} = {res:{ralign 8:.5021994}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:1.female} = {res:{ralign 8:.4978006}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:0.white} = {res:{ralign 8:.2162757}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:1.white} = {res:{ralign 8:.7837243}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:0.hispanic} = {res:{ralign 8:.8958944}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:1.hispanic} = {res:{ralign 8:.1041056}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:0.college} = {res:{ralign 8:.5960411}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:1.college} = {res:{ralign 8:.4039589}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:0.hhi_num} = {res:{ralign 8:.1788856}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:1.hhi_num} = {res:{ralign 8:.2961877}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:2.hhi_num} = {res:{ralign 8:.3482405}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:3.hhi_num} = {res:{ralign 8:.1539589}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:4.hhi_num} = {res:{ralign 8:.0227273}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:0.pid7} = {res:{ralign 8:.2162757}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:1.pid7} = {res:{ralign 8:.1480938}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:2.pid7} = {res:{ralign 8:.1231672}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:3.pid7} = {res:{ralign 8:.1517595}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:4.pid7} = {res:{ralign 8:.0755132}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:5.pid7} = {res:{ralign 8:.164956}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:6.pid7} = {res:{ralign 8:.1202346}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:0.employed} = {res:{ralign 8:.1678886}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:1.employed} = {res:{ralign 8:.1246334}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:2.employed} = {res:{ralign 8:.4941349}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 24:3.employed} = {res:{ralign 8:.2133431}} {txt:(mean)}

{res}{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29} Delta-method
{col 17}{c |}      dy/dx{col 29}   std. err.{col 41}      z{col 49}   P>|z|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0.humanitaria~m{col 17}{txt}{c |}  (base outcome)
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.humanitaria~m {txt}{c |}
{space 7}_predict {c |}
{space 13}1  {c |}{col 17}{res}{space 2}-.1661872{col 29}{space 2} .0239067{col 40}{space 1}   -6.95{col 49}{space 3}0.000{col 57}{space 4}-.2130434{col 70}{space 3}-.1193309
{txt}{space 13}2  {c |}{col 17}{res}{space 2}-.0217739{col 29}{space 2} .0043444{col 40}{space 1}   -5.01{col 49}{space 3}0.000{col 57}{space 4}-.0302888{col 70}{space 3} -.013259
{txt}{space 13}3  {c |}{col 17}{res}{space 2} .0225181{col 29}{space 2} .0047444{col 40}{space 1}    4.75{col 49}{space 3}0.000{col 57}{space 4} .0132193{col 70}{space 3}  .031817
{txt}{space 13}4  {c |}{col 17}{res}{space 2} .0615524{col 29}{space 2} .0093635{col 40}{space 1}    6.57{col 49}{space 3}0.000{col 57}{space 4} .0432003{col 70}{space 3} .0799045
{txt}{space 13}5  {c |}{col 17}{res}{space 2} .0439129{col 29}{space 2} .0066627{col 40}{space 1}    6.59{col 49}{space 3}0.000{col 57}{space 4} .0308543{col 70}{space 3} .0569715
{txt}{space 13}6  {c |}{col 17}{res}{space 2} .0368076{col 29}{space 2}  .005748{col 40}{space 1}    6.40{col 49}{space 3}0.000{col 57}{space 4} .0255416{col 70}{space 3} .0480735
{txt}{space 13}7  {c |}{col 17}{res}{space 2}   .02317{col 29}{space 2} .0041048{col 40}{space 1}    5.64{col 49}{space 3}0.000{col 57}{space 4} .0151247{col 70}{space 3} .0312154
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 81}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}.  eststo ordinal_coup_c_dydx, title("Coup")
{txt}
{com}.                 
. coefplot        (ordinal_contribute_c_dydx, label(Contribute))                                          ///
>                         (ordinal_coup_c_dydx, label(Coup))                                                                      ///
>                         (ordinal_threaten_c_dydx, label(Threaten))                                                      ///
>         , recast(bar) vertical barw(0.25)                                                                                       ///
>         ciopts(recast(rcap) color(gs8)) citop                                                                           ///
>                         xlab(1 `""Strongly" "disapprove""' 2 "Disapprove"                                       /// 
>                         3 `""Somewhat" "disapprove""'           4 "Neither"                                     ///
>                         5 `""Somewhat" "approve""' 6 "Approve" 7 `""Strongly" "approve""',      ///
>                                 labsize(tiny))                                                                                                  ///
>                 title("{c -(}bf:C) Predicted Changes in Support{c )-}",                                                   ///
>                                 size(medsmall)) yline(0) ylab(`myla',labsize(vsmall))                   ///
>                 subtitle("{c -(}it: Humanitarian Minus Interest{c )-}", size(small))                              ///
>                 name(gph_dydx_selected, replace)                                                                                ///
>                 legend(rows(1) ring(2) pos(12))         
{res}{txt}
{com}.                 
. replace strategy = "Concession Before" if strategy == "negotiate"
{txt}variable {bf}{res}strategy{sf}{txt} was {bf}{res}str10{sf}{txt} now {bf}{res}str17{sf}
{txt}(1,515 real changes made)

{com}. replace strategy = "Concession After"  if strategy == "concession"
{txt}(1,515 real changes made)

{com}. replace strategy = "Coup" if strategy == "coup"
{txt}(1,515 real changes made)

{com}. replace strategy = "Threaten Sanctions" if strategy == "threaten"
{txt}variable {bf}{res}strategy{sf}{txt} was {bf}{res}str17{sf}{txt} now {bf}{res}str18{sf}
{txt}(1,515 real changes made)

{com}. replace strategy = "Contribute" if strategy == "contribute"
{txt}(1,515 real changes made)

{com}. 
. * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * 
. * Summary Plot
. * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * 
.                 
. catplot  if humanitarian_treat_num == 1                                                                                 /// 
>         , over(human_DV_trichot, label(labsize(*.65)))                                                  ///
>         over(strategy, label(labsize(2)))                                                                                       ///
>         asyvars stack                                                                                                                           ///
>         bar(1, bcolor(ebg)) bar(2, bcolor(dimgray))                                                             ///
>         horizontal percent(strategy)                                                                                            ///
>         ytitle("Percent")                                                                                                                       ///
>         blabel(bar,format(%4.1f) size(2) position(center))                                                      ///
>         legend(position(1) rows(1) size(vsmall))                                                                        ///
>         name(gph_catplot_humanitarian1,replace)                                                                         ///
>         title("{c -(}it:Humanitarian{c )-}", size(small))
{res}{txt}
{com}.         
.         
. catplot if humanitarian_treat_num == 0                                                                                  /// 
>         , over(human_DV_trichot, label(labsize(*.65)))                                                          ///
>         over(strategy,label(labsize(2)))                                                                                        ///
>         asyvars stack                                                                                                                           ///
>         bar(1, bcolor(ebg)) bar(2, bcolor(dimgray))                                                             ///
>         horizontal percent(strategy)                                                                                            ///
>         ytitle("Percent")                                                                                                                       ///
>         blabel(bar,format(%4.1f) size(2) position(center))                                                      ///
>         legend(position(1) rows(1) size(vsmall))                                                                        ///
>         name(gph_catplot_humanitarian0,replace)                                                                         ///
>                 title("{c -(}bf: A) Raw Support for Strategies{c )-}")                                                    ///     
>         subtitle("{c -(}it:Interest{c )-}", size(small))
{res}{txt}
{com}.                 
. 
. grc1leg gph_catplot_humanitarian0 gph_catplot_humanitarian1                                             ///
>         , rows(2) name(gph_combined_temp1, replace)                                                             
{res}{txt}
{com}. 
. gr combine ologitcoef_small gph_dydx_selected, rows(2) name(gph_combined_temp2, replace)
{res}{txt}
{com}. 
. gr combine      gph_combined_temp1  gph_combined_temp2                                                          ///
>         , rows(1) name(gph_combined_ultimate_alt,replace)                                                       
{res}{txt}
{com}.         
. gr display, ysize(9) xsize(9)   
{res}{txt}
{com}. 
. **********************
. * Main Paper Figure 3
. 
. gr export "${c -(}MyProject{c )-}00 Main Figure 3.pdf", replace
{txt}{p 0 4 2}
file {bf}
~/Dropbox/0001 Academic Projects/Ongoing/0155 Voting Abroad Purpose/Replication/Upload/00 Main Figure 3.pdf{rm}
saved as
PDF
format
{p_end}

{com}. 
. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}/Users/rpm47/Dropbox/0001 Academic Projects/Ongoing/0155 Voting Abroad Purpose/Replication/Upload//Experiment1Log.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}12 Jan 2025, 09:29:36
{txt}{.-}
{smcl}
{txt}{sf}{ul off}